"The Future of Finance" - EDHEC Speaker Series

[Music]
EDHEC Business School
EDHEC Speaker Series - The future of Finance
Impact investing

Ayako Yasuda
Professor of Finance - University of California, Davis
Graduate School of Management

Tuesday, 2 May 2023
6 P.M - 7 P.M Paris time

Emmanuel  Jurczenko
Okay, so let's go ahead. So hello everyone and welcome for the last session of the second season of the EDHEC Virtual Speaker Series on the future of Finance. I'm Emmanuel Jurczenko. I'm the director of the Graduate Finance program here at EDHEC. So today, first, it's my pleasure to be with my colleague Enrique Schroth. So Enrique is professor of Corporate Finance and very, very happy and very proud, in fact, to have our guest speaker for today's station, which is Ayako Yasuda. So Ayako is professor at the Graduate School of Management of University of California. She's a very well-known, internationally renowned financial economist. She's known for her work on private equity venture Capital Finance, Sustainable Finance that is the topic of today. Her work has been published in top scholar journals like Journal Finance, TFE Review Financial Studies, and she's also the author of textbooks, which is a real reference in the world of venture capital. And we are very fortunate to have Ayako today to talk about Sustainable Finance and more precisely, to talk about impact investing, how to define impact investing, how to differentiate impact investing with respect to, for instance, ESG investing. So, without further ado, I let the floors to Ayako.

Ayako, the floor is yours.

Ayako Yasuda
Thank you Emmanuel, and thank you Enrique, for being here. Hello everyone. My name is Ayako Yasuda. I'm a professor of Finance at the University of California Davis, which is in Northern California. And I'm going to talk to you today about impact investing. So let me share my screen. It's working? Okay.

Emmanuel
Perfect.

Ayako
Okay, great. So I'm going to organize my talk in three parts, but let me first move this. Okay? Yeah. So it's broadly about Sustainable Finance, but I'm going to focus about the following questions. When we say sustainability, is it a sustainability of what? Is it sustainability of the planet or sustainability of your portfolio value? That's one key distinction. Another key distinction is how do you try to be responsible? If you decide you're going to be a responsible investor, do you going to really pay attention to what principles you're following when you're making investment decisions? Or you're going to care really about what is the outcome of my investment decisions? And those questions, answering those questions really clarify in many ways how do you be sustainable? And I'm going to define three different kind of motivations that motivate people to allocate their money to so called impact investing or sustainable investing.

To be more specific, I'm going to first, in my first of the three parts, really define what distinguishes impact investing from ESG or ESG-aware investing and also traditional socially responsible investing. In the second and the third part, I'm going to talk more about research that I have done on impact investing and more recent research that I'm doing on sustainable investing. So you probably don't need to be reminded, but amount of money involved in sustainable investing these days is really huge. So amount of capital managed by investors who pledge to the UN principle of responsible investing has now surpassed $100 trillion. I'm sorry, I'm going to use dollars, not Euros. Just I'm more used to thinking in dollars, but it's not going to be very different in Euros as you know, given the exchange rate. So what do investors behind $100 trillion want to do with this huge financial muscle? That's really the important question. And this is the preview of the insights that I will provide. First, impact investing has two really main characteristics. It's exclusively dual objective and it's also activist. So it serves investors who want pro social, pro environmental outcome and you derive happiness or utility from it when you generate positive externality.

And second, I find that investors who invest in impact VC funds, they care about this outcome enough that they are willing to accept lower financial return and expectation. This is really important because this tells us that impact investing in not just about branding, not just about sort of a marketing gimmick, but actually investors make this conscious decision. And third, this is about VC funds and VC funds is private. So can we do in fact investing in public markets today? And here I'm going to find that tell you that many public funds that are marketed under this ESG fund umbrella today are ESG-aware funds and not impact funds. So I highlighted in red here the distinction between what is impact and what is ESG-aware is key. So I'll talk about that now. So this distinction between these two categories of sustainable funds is a source of lot of tension and confusion and I'll try to demonstrate this in several different ways. So I'm going to now present three archetypes of sustainable investors. Sam, Inger and Ellen. So think about which type are you as I describe these three people.

So Sam, I says I object to private prisons. This is something in fact, we do have in the United States and a lot of people find it abominable. It bothers me if a mutual fund that I hold in my retirement account invests in shares of a private prison operator.

Inger says I feel passionate about making access to clean water more equitable. So I flex my financial muscle to back companies that promise to make this happen.

Ellen says I sense this huge shift in the whole economy towards decarbonization and I want to climate proof my investment portfolio against this new risk. Some people call it stranded asset risk.

So which type of investors resonates the most with you? Are you Sam, are you Inger or are you Ellen? Or maybe a combination of the two. Inger is an impact investor. I categorize Inger as an impact investor. Ellen is what I categorize as ESG-aware investor.

So now I'm going to define impact investing. It has two main characteristics that are important. One, impact investing is explicitly dual objective. So it's a for profit fund that aims to generate both positive social and environmental impact as well as a financial return. Impact investing is also activist. So it aims to generate this externality actively and causally, not just through passive allocation, but typically through ownership and active governance of the companies that they invest in. And I say non-pecuniary preferences here, this is just a fancy word for indicating that some investors don't invest just for financial risk and return, but they actually want to either generate or feel good about what they're doing. So something known financial, either social or environmental, they care about it.

And now these three type of investors motivations, now you can see how they map to different financial products. So Sam wants to alleviate conscience and that maps to socially responsible Investment or SRI Fund. How they do it is mostly through negative screening. So you do not invest in stocks that you think are bad. So categorical screening out of SENS Stocks, in this case private prisons, but it could be fossil fuel companies.

Inger wants to allocate to solution. So this is a positive screening in and the products that cater to this type of investors are impact funds and also I think of some Activist ETF or Activist Funds as also falling into this impact investing category. So you invest in impact startups if it's a private fund or you do proxy voting to impact or nudge the company's management's decisions on policies.

Ellen wants to avoid risk. So what are corresponds to Ellen's desire is a passive ESG-Fund and these funds hold high ESG-rated stocks and they Re-balance the portfolio. So now I distinguish between impact and ESG-aware in this way.

Another way of organizing my thoughts and these three types that I find very useful is to think of two vectors. One is what is the investor objective and what is the belief about your social responsibility? So investor objective can be single or dual. If you're a single objective investor, you're only trying to do financial risk and return optimization. Whereas if you have some non financial desire utility then you are in the upper level. Then another vector is what is your belief about social responsibility. And here if you care about the output of your investment, and that's philosophically that means you're a consequentialist, you care about the consequence of your investments, then that makes you an impact investor like Inger. Whereas if you're more of an input based thinker, you think about whether your investment is inherently good or bad at the time of decision making. So philosophically that's associated with Kant, Emmanuel Kant, the same first name as Emmanuel, it's a famous philosopher and you also describe this as deontological. So if you are more thinking about principles then that fits socially responsible investing like Sam.

And key point here is only impact investing is designed to generate a change and positive externality by corporations or corporate actions. I think that's very important. This is just a different way of organizing the same thoughts that I use in a figure in one of my papers.

So now I present this dilemma between two investor motivations. One is you want to do well by using ESG information as an input to pick stocks. Another is I want to do good to benefit broader society and environment. One fits ESG-aware, another fits Impact. And these two incentives don't naturally coincide. So it's hard to do both at the same time in the same portfolio. And yet the way we talk about ESG, we often mixes up the two and this creates confusion and tension.

If you're an accounting person, you might also have heard materiality-based ESG-investing. And this is material in accounting sense. So if you're ESG-aware investor, you care about ESG as long as it's material, meaning it actually impacts the value of the company or financial value of the future cash flow. It's been generated. So here you are materiality or single materiality-based ESG investors, then your goal is still singular. But impact investing is dual objective and materiality-based ESG. The information you use are often ESG ratings. For example, ESG ratings created by MSCI. So what do MSCI ESG ratings measure here? I just went to their website and look at what they aim to measure. And the key here is ESG Ratings aim to measure financially relevant ESG risks and opportunities. And it uses this methodology to distinguish leaders and the laggers according to their exposure to ESG risks and how well they manage those risks. So it's pretty clear from the language here that geese ratings don't measure how much good the company produces, but it's about the resilience of the company against this risk.

And finally, ESG-aware sustainability is a survivalist sustainability. So the world may be at risk on fire, floods, et cetera, but I want to make sure that my portfolio is survives, is preserved. Impact sustainability in contrast, is communal. I want my portfolio to help the rest of the broader society and the planet. They both serve legitimate important purposes for somebody like Ellen, she would want ESG-aware sustainability. Somebody like Inger would want impact sustainability. A single person could want part of her portfolio to be ESG-aware managed and another part to be impact managed. I think that's potentially quite common.

Okay, so here's just one example of an ESG-aware fund in fact. It's called iShares ESG Aware and it is based on ESG ratings. So what does this fund hold? Actually, tech stocks were very prominent in this fund. And that makes sense because tech stocks are profitable, they treat employees well and has small carbon footprint. But does it generate positive impact? Probably not.

Here is a very different fund example called Engine number 1 Fund. Engine Number 1 fund is originally a hedge fund. So this hedge fund took a part of activist kind. It tried to propose four new directors for ExxonMobile. ExxonMobile rejected this proposal. But then Engine number 1, despite having less than 1% ownership, convinced the three largest shareholders, BlackRock, StateStreet and Vanguard to go along with them. And because together they own about 20% of ExxonMobile shares, they managed to elect three out of the four directors that Engine number 1 fund proposed, which was a really big upset for the incumbent management and the CEO. So the long term goal here is to use this proxy power to pressure ExxonMobile to shift faster to renewable energy, adapt for climate, transform its business. And Engine number 1 also now offered ETF funds that uses similar strategy.

So these two funds, I mentioned this because you can see that if you are ESG-aware fund, excluding fossil fuel stocks, holding tech stocks to avoid the carbon risk, that's an ESG-Aware choice for your goal. But if you are Impact fund, then holding dirty stocks and force the dirty stock to become cleaner, that's an Impact choice. And you can see opposite type of portfolio choices. And I think I mentioned this already. The output versus input based social responsibility is what distinguishes the two types of social responsibility, right? Consequentialism is aligned with impact investing. You measure how successful or how good you are by actually measuring the outcome. If you are Deontological or Kantian investors, you don't need to do that because you are done when you make the decisions. Do I stayed away from what I think of as bad stocks.

Okay, now I'm going to tell you about my paper on impact investing. I think I'm doing okay on time. So impact in private equity venture capital space are growing rapidly. So in this 2021 paper we study particularly the VC funds with this explicit dual objective. VC funds are always activists, so that fits the impact investing definition. The question is, do investors actually allocate to impact because they are willing and are they willing to pay for this impact when they do that?

So what is an typical VC impact fund? Here's an example. South Asia Clean Energy Fund is an Impact Fund. Investors include limited partners like IFC, which is a part of the World Bank. So they provide capital and they back startups or private companies. So Princepipes ESDS, these are both Indian companies. Princepipes manufactures and markets water pipes and it's a for profit company. The environmental impact here is the water conservation. ESDS is a data management center. Data center? Data center consumes a lot of energy. So here the environmental impact is to be more energy efficient, dramatically more. So in addition to the financial return it creates, it aims to create this environmental externality.

In our sample of impact funds, these are the type of categories that they aim. So you see environmental impact, poverty alleviation, minority and women and so forth. These are the types of investors who invest. This is the kind of industry concentrations. So VC funds concentrate a lot in IT and Healthcare impact funds less. So this is the mathy part of the paper. But what we are basically trying to do is to measure the willingness to pay for impact as a ratio of how much you care about return and how much you care about impact. And now we can express willingness to pay in terms of the percentage of return that you're willing to trade off for the impact. And we find that number to be positive on average about 2 to 3%. So you hear 3.7% IRR here, 2.5 IRR on average. But average doesn't mean everybody has this willingness to pay. It really varies a lot. So it varies by geography. I'm in US. So North American investors willingness to pay is positive but smaller compared to Europeans and emerging market investors willingness to pay higher. That's very interesting also by investor type. If you're development organizations, banks and insurance companies and public pension funds, you have positive willingness to pay. If you are university endowment you don't have positive willingness to pay.

And what kind of investor attributes explain this organization that have mission organizations that pledge to the UNPRI principles of responsible investments, that has higher willingness to pay. If you have some legal restrictions, you have negative willingness to pay. And this mostly applies to US investors that are bound by more strict fiduciary duty. European investors are not bound in the same way, so they seem to have higher willingness to pay.

And finally, impact investors are willing to pay more when the impact funds invest in categories that have a higher public good or higher externality content. So environment for example, or poverty reduction or minority and women. So this really also is consistent with our interpretation here that it's because these are important social goals and you have high utility, non pecuniary utility, it's making them happier when you see these goals achieved and you're more willing to trade off.

So that's the summary of the paper here. Investors are willing to pay for impact. It's not just about marketing something as impact, it's not just a greenwashing here and different type of investors, Europeans, Development organizations, UNPRI signatories they are more willing. So I think that's the main takeaway here.

Now I'm going to tell you about what kind of funds are available in public markets, which I think is important because for most regular people we have access to public markets far more than to private markets, right? So from here on this is very much a current research that I'm doing. So I'm still trying to address these questions. These are not done research, but it's more fun for me to talk about this as well. So I have one current work called Survivalist Consequentialist and Deontological : the Three Colors of Sustainable Investing. And I think the motivation for this work is hopefully I explained this, there is this confusion between ESG-aware, impact and the SRI goals of sustainable investing. These goals are not very well labeled and not very well understood. So if we don't know which type of funds do, which kind of sustainable investing, there is a pretty big risk that, let's say, a family wants to invest in Impact Fund but ends up investing in an ESG-Aware fund. And that's a misallocation of the capital and also a missed opportunity for the actual impact that can be generated by fund managers who take their money.

So it's very important that we are matching the investor objectives with the actual type of funds that people want to invest in. And if you look up Sustainable funds today, all three types are called Sustainable funds and there is no easy way to distinguish between them. So what we do is we develop a machine learning based model that classify these funds using actual text that these funds provide. So every fund has a prospectus and prospectus has investor principal investment like what the investment objectives and strategies are. So we analyze this text sentence by sentence and use machine learning model to classify them. So this figure you see here is our result and these are funds that are considered sustainable by Morningstar. And the reason why the three circles of ESG-Aware funds, SRI funds and Impact funds, why the circles overlap is because we are categorizing them by sentences and some funds have sentences that correspond to SRI and ESG-Aware or Impact and SRI and et cetera. You can see that there is a big overlap between ESG-Aware and SRI. So many funds do both of those goals and less overlap with the impact funds. Impact funds are a little bit more isolated and once we categorize these funds we can then study how do these funds differ in what kind of stocks they hold and how they make those portfolio balancing decisions to impact generate impact. And our finding, which is pretty still ongoing, is that ESG-Aware funds green their portfolios whereas Impact funds green the planet. So that is consistent with their investment goals but have very different implications for the portfolio.
I want to provide a few examples for these type of funds and just a disclaimer here is that it's just to illustrate the kind of funds that are possible out there. It's not an investment recommendations in any way. I don't endorse or object to any of these funds just to show how we did the research. So yes, the Aware Fund example here is a Fidelity fund and by the way, these are all US funds. This is what I have data access to. But I would love to also do similar research on European funds. I'm in the process of looking at European funds as well. This fund does portfolio construction using MSCI ESG ratings. MSCI Ratings as we saw is about ESG materiality. So this is designed to mitigate portfolio ESG risk, not to generate externality.

This is a SPDR fund example, it's an ETF and it's called Fossil Fuel Free ETF. So it categorically excludes any companies that have significant fossil fuel related activities and contributes to emissions. And since it's about just categorically negatively screening out anything that relates to fossil fuel, but it's not really trying to track what kind of impact you're generating. This is an example of an SRI fund or more input based socially responsible fund.

And the third Impact fund example, this is Engine number 1 Transform Climate ETF. This ETF is designed to allocate capital to induce transition to renewable and externality generation. And you can see that in the language. But interestingly, if you look at Morningstar's sustainability ratings, it actually has the lowest sustainability rating by Morningstar. Why is that? Because it holds stocks in high environmental impact sectors and dirty sectors. So for example, John Deere, it's an agricultural equipment company, lithium, General Motors. So interestingly, this would be considered not sustainable by Morningstar's rating methodology. In just a few minutes, I have, I want to say a little bit about the European regulation on this and I think this is a really interesting topic.

So SFDR or Sustainable Finance Disclosure Regulation. This is a new EU regulation for financial products. And now financial products in Europe are required to be categorized as Article 6, 8 or 9 funds. Article 6 funds don't incorporate ESG in any way, whereas 8 and 9 do, and Article 8 funds incorporate environmental and social characteristics. Article 9 is a fund where your environmental and social sustainability is a primary goal of the fund. It is not quite clean or clear to say completely what is Article 8? What is Article 9? A lot of people are talking about how to do this. So the interesting questions that I think about is one, how will this classification map to the three investor goals? And two, will Article 9 funds be impact funds or not? And I don't have the answer to these questions. I think these are really important questions. And so what I would like to study, if I have good data, is to try to answer these questions. So let's see ESG-Aware, SRIand Impact funds with overlapping circles. My conjecture, or my hypothesis about Article 8 funds is that these are allowed to use negative screening both to mitigate risk and to avoid sin stocks. So one hypothesis is Article 8 funds are likely to have mix of ESG-Aware and SRI goals, whereas Article 9 funds, they are allowed to employ positive screening. Alignment with the UN SDGs is a primary goal of these funds, but it doesn't really say much about whether you are input based or output based. So you can be deontological or consequentialist in your methodology to qualify as Article 9 funds. So Article 9 funds, my hypothesis would be they're likely to be a mix of SRI and Impact funds. So how much impact Article 9 funds generate would depend on this mix. How much is SRI? Is it all SRI or some mix of SRI versus Impact? Another thing that I think is key is the impact of the principal adverse impact that requirements to report these PAIs, whether that incentivize fund managers to be more of an SRI fund versus impact funds, I think these are going to be key. This is my prior.

So to conclude Impact investing is explicitly dual objective and activist investors in impact VC funds are willing to pay for impact which I think as a society if that's a significant portion of sustainable capital then sustainable finance could be a pretty significant part of the solution to the world's largest problems. Like climate change, like social inequality. Governments and nonprofit organizations traditionally are supposed to be the actors who try to solve these problems. But more and more there is a call for corporations and financials to play a role. Now to offset that second result a little bit, we also find that many funds that are called sustainable that are available to regular investors today are not impact funds. In fact they are trying to achieve a different goal that's not really meant to generate impact. So as we debate the role of sustainable finance in transition to net zero and trying to tackle the inequality in the society, I think we need to reckon with the fact that much of the $100 trillion of capital that is under management by so-called sustainable fund managers is not mobilized to generate externality. And is that exactly what investors want? Or do investors actually want to generate more impact but the products are not there to meet their unmet demand? Those are really important questions that we want to have a better understanding and we are not that year yet. There yet, but I think many of us are working towards answering those questions.

So this is the end of my presentation. Thank you. And now I'm ready for Q and A.

Emmanuel 
Thank you Ayako, thank you very much. So now I pass to my colleague Enrique.

Enrique Schroth
Thank you. Thanks a lot, Ayako, for that presentation.

Ayako
My pleasure.

Enrique
I learned a lot and clearly all the attendants have. I would like to ask you two questions. Let me first state the two and then you can choose which one to answer. One I think you can answer because it's the work you've done. The other one is purely speculative. I just want your opinion. And you may not have one yet because the data, I don't think it's available, but it'll be good to know. Okay, so the first is straightforward and it's related to the measurement of performance in private funds, which is incredibly difficult. And now in the context of activism, which is pertinent for impact investment, for activism in private firms, it's not nearly as hard. We see what the price of entry is for the activist, we see the short term price reaction to the announcement of an engagement, we see the long term performance. So we have a good sense of what the returns are to the activist. How much value is created in the transaction. Now, in a private transaction like the ones you have, we know what the challenges are in the numbers that you've given us. The 2% to 3% discount that a fund accepts, is this based on data from entry to exit or is this based on the cash flows that they get during their investment whilst there's still investment? Some detail here might be helpful because I'm wondering to what extent that discount they are taking that we're witnessing, thanks to your paper, is a number that will be revised, right?

Ayako
Sure. So thank you for that question. It is true that adjusting for risk in private equity performance is difficult because we don't have this daily observed price to apply standard model like CAPM model, extension of CAPM model to distinguish between alpha and beta. So we do a few things in our paper and this is standard in private equity venture capital research as well. Private equity VC Funds have a particular vintage year. That means you start in a particular year and then you run the fund for on average ten years and then you can extend it for a couple of years. So if you start a fund in year 2000, you have an opportunity to invest from 2000 to 2005 and then hold it until 2010. So this is a particular economic period. Whereas if you start the fund in 2009 and hold it until 2019 or to start a fund in 2019 but then hits pandemic right away, that's a completely different time period to have your investment opportunities in. So we in the private equity industry do this benchmarking by vintage year. So all funds that start to start, let's say an American VC Fund or European VC Fund in a particular vintage year and then hold it for the next ten years, you are put in a cohort, like you as a student is a cohort of particular year, with a particular graduation year. Funds are in the cohort. So what we do is we rank the performance of the fund in the same cohort and do a percentile ranking. And the performance can be ranked by either IRR, which you know, or something called multiple. And multiple is basically money in and money out of the fund throughout the fund's life. And we try to look at mature funds. So it's a lifetime performance of the fund and you rank them. And if you're the top fund in your cohort, regardless of your IR itself, you are considered a top performer. And this is very important because some vintage year, if you have any positive return, you are in a top performer. Whereas in a very booming market cohort, you have to have more than 20% to be a top performer. So we do that adjustment by percentile rank. And when I report two to 3% IRR, what I'm doing then is to basically convert that percentile rank like your two percentile or five percentile or 25 percentile to the IRR by using all historic data and averaging the extra return in IR, you earn from the average. So everything is in terms of excess IRR and you have this overall distribution of excess IRR and I'm mapping it to so if you're a two percentile on average, that means you had excess IRR by 14%, et cetera.

So I'm measuring it by excess IRR, but first by doing all the analysis in percentile ranking and then converting it to excess IRR. So that way you are at least accounting for the differences in the type of risk and basically the investment opportunities that you had when you invested. And it is from entry to exit for the whole fund.

Enrique
Okay

Ayako
You had a second question Enrique?

Enrique
Yeah. So it's about the trends. You showed us the trend in the total amount managed by impact funds. And there's an upward trend, but I suppose well, we know there is also an upward trend on the amount managed by the ontological investors, right? Or on behalf of the ontological investors. Do you have a sense of which is amount is growing faster or the proportion of dollars destined to impact versus ESG-aware funds? And of course we are still in a transition, I suppose, right. And my question is the following given that the impact investing underperforms, let's say that way there's a 2 to 3% return that you give up, right? Are they going to survive? Suppose we are naturally deontological in our preferences. Are we having to become consequentialists at some point? It's a question about the long run steady state of these two forms of investment. I don't think anyone has an answer, but I just wonder what your thoughts are.

Ayako
It's a really great question, I think to so right now, if the status quo continues, I don't think impact would scale very much because right now the fund categories are all mixed and essentially you are benchmarked against ESG Funds as Sustainable Funds. And since ESG Funds, ESG-aware Funds are primarily about financial performance, right? So you are ranked against ESG-aware Funds in performance, financial performance, but you're not really ranked against ESG-aware funds in the impact because there's no such objective measurements yet. So people, as long as they feel good in investing in Sustainable Funds, then it is, I think overall if you're as a fund manager objective is to get as much capital as possible into your fund and get the most amount of fees. I think there is a conflict of interests between Sustainable Fund managers own incentive, which is more self interested, versus the overall objective that individual investors might have. I think the equilibrium outcome will be that the biggest and the most successful funds are going to be ESG-aware Funds because they will have better and objectively measured financial performance and they get to say we are also sustainable.

And right now ESG ratings, sustainability ratings provided by the industry, don't differentiate between somebody who's doing ESG-aware investing from somebody who's doing Impact investing. Part of my research with the machine learning model is trying to provide a better metric of which ones is actually generating impact versus somebody who's not trying to generate impact, so that households or individuals who actually want impact can make a more informed decisions. And once people make informed decisions, we have a better sense of how many of us actually want to generate impact versus how many of us just want to climate proof the portfolio. Right now we can't really see it because there is this lack of clarity and transparency. So if your question is if nothing happens in terms of classification, what would happen? Then my answer would be yes ESG-aware Funds are going to be the most biggest. Now, European regulation is also interesting because I think article 8 and 9, a lot of people would say that's trying to provide that clarity and I would say this is a good first step. At least I think in terms of differentiating between ESG-aware and Impact. I agree, 8 is more ESG-aware, 9 is more Impact.

The ambiguity that I still think is potentially problematic. And Enrique, you're alluding to that when you say the ontological is the 8 and 9 classification doesn't distinguish between input based deontological fund and output based Impact fund and financial research by others. And you can also sort of think through the ontological fund may satisfy you a neat desire to alleviate your conscience, but it's not going to generate impact. It's not designed to generate impact, it's not designed to actually solve climate change. But you just feel good if you don't hold dirty stocks.

Emmanuel
And at the society level, Ayako, what we need is we need inspect to make a difference. So we don't need so much to have a net zero fund. What we need is a fund that will change.

Ayako
Yeah, that's why if you focus more on the change part, then you might actually want to hold bad stocks, dirty stocks and change it. But if you focus so much on being clean on day one, then everybody wants to hold clean tech, big tech stocks that already don't have low carbon footprint. But we don't need to hold tech sectors to solve climate change. Like tech sectors already are going to be net zero without our help. We need to provide our assistance or nudging to companies that are not pledging net zero. And my worry is that the European regulation, it's very well intended, but it might actually incentivize fund managers to just hold already clean stocks and say thatnd and also the disclosure requirement of the adverse impacts will incentivize fund managers to rush to hold stocks that are already very good in disclosure and shy away from holding stocks that are not great in disclosure or emerging market stocks that we don't have data. And you end up misallocating capital to companies that don't need any nudging and neglect the companies that do need a lot of nudging. So that part of the policy work, I don't have strong evidence, but my hunch is that European regulation is definitely ahead of the US regulation but still has those issues that need to be resolved.

Emmanuel
Thank you. I have from my side a question regarding the measure of impact. So can you tell more about how actually do we measure the so called additionality? So the positive impact? Because back to your research, I mean, it's good that in the perspectives you say that you want to do an impact, but what matters is are you really making this positive impact? So can you tell us more about how things are in practice? Where do we stand, what are the challenges, if any, with respect to additional? How does that works actually with respect to impact funds?

Ayako
No, I'm glad you asked this because this comes up in my discussion with practitioners a lot and there is this sort of irony a little bit there because I know that European investors LPs now really insist on additionality. And some of the US GPS that I talk to want to get capital allocated from LP but they are not sure if they can meet the requirements of additionality and they believe that what they're doing is impact investing but they are not sure if they can qualify for it. So, for example, there was a fund manager that I was talking to who do ecological restoration and this is an ecological restoration done for the clients who need to comply with regulatory requirement in the US for Clean Water Act. So if you develop a land that impacts watershed quality, then you need to restore it acre by acre. And this generates business opportunities for fund managers to do this type of restoration project. So it has monetary value because you need to comply with the regulation. And each project definitely has this environmental positive impact because you are restoring land that wouldn't be restored otherwise. But some European LP would object because well, you are restoring impact, but there has to be an original sin that this client actually damaged environment. So you're just restoring what was damaged to net zero. That doesn't really meet our criteria. So this GP thinks that what they're doing is always there's a positive impact and it's also sustainable because we can't just not build any houses or not build any roads. We do have to meet the population's demand. So there's going to be some environmental impact and it's really good that this regulation requires restoration. But if that's not considered additional, then we are not really addressing the actual need for people to still live in houses and have roads and things of that nature or mine mineral that we really need for green technology. So, this is a really interesting point. I think additionality being thoughtful about it is really important. But if you make the regulation so stringent that some funds which they think is doing impact generation can't meet the qualification and be starved of capital, then we need to also think about the unintended consequence of the regulation. So that's where my thinking is right now.

Emmanuel
Okay, so, thank you very much Ayako I think we are close to the so very insightful. So thank you. Thank you for sharing your analysis. So obviously looking forward to your paper on machine learning.

Ayako
Thank you Emmanuel.

Emmanuel
I hope that we have the pleasure to have the first presentation of it. Thank you, Enrique.

I just take the opportunity to say that this was the last session of this 7th season, but we will be back we will be back in September for third season with other nice speakers on Sustainable Finance and private markets topics. So, thank you very much. So let's keep in touch and looking forward next year. So, thank you, Ayako you're welcome. See you. Bye bye.

Ayako
Everybody. Enjoy.

Make an impact
EDHEC BUSINESS SCHOOL
[Music]
 

[Music]
EDHEC Business School
EDHEC Speaker Series - The future of Finance
Do corporations need a purpose?

Alex Edmans
Professor of finance - London Business School

Tuesday, 18 April 2023
6 P.M - 7 P.M Paris time

Laurent Deville
Hello, everyone. I'm Laurent Deville, Academic Director of the Masters in Finance EDHEC Business School. And I'm very pleased to welcome you all to this session of our Future of Finance Speaker series. Today, I temporarily replaced the regular host, Emmanuel Jurczenko. Let me remind you briefly how we'll proceed. I'll introduce our speaker before I leave the floor to him and then to the moderators, Hamid Boustanifar, professor of Finance at the EDHEC Business School, and Marine Kazarola and Ming Hug Singh, two of our amazing students from the MSI Corporate Finance and Banking. I'll be the timekeeper, and I may use my privileged position to ask one or two questions from the chat. Don't hesitate to use it. So our speaker today is Alex Edmans, and he's going to question whether companies need a purpose. So let me introduce him in a few words. Alex Edmans is professor of Finance at London Business Schools, and he serves there as academic Director of the center for Corporate Governance. Not only is Alex a very successful academic with his expertise in as diverse fields as corporate governance, responsible business investment strategies, behavioral economics, time management, and the use and misuse of data and evidence. He also serves the academic community as Managing Editor of the Review of Finance.

But Alex does not only speak to the financial academic community, he addresses largely society via articles and books. And I asked the question to Alex before, do people still read books? And it seems to be the case. And one of those is the famous grow, the Pie, that has just been translated in French and that, I think, will serve as the core of today's talk. The rumor is that Alex is also an amazing teacher and a very impactful speaker. So it seems that his merits are almost countless. So I'm going to stop here, or there will be no time remaining for his talk. Alex, I didn't want to put too much pressure on your shoulders, but I wanted to be very clear about our expectations here. So the next 30 minutes are fully yours, and, well, we'll have the opportunity to discuss afterwards with Hamid, Marin and Ming Hu about its contents. Thank you very much, Alex, for being here with us today.

Alex Edmans
Merci beaucoup Laurent pour cette invitation, je suis très heureux d'être ici. J'ai commencé a apprendre le français à l'âge de 7 ans parce que j'ai visité une école Montessori, normalement c'est à l'âge de 11 ans mais maintenant mon français n'est pas très bien, c'est très mauvais, je n'ai pas l'occasion de pratiquer, alors je m'excuse je dois présenter en anglais.

So what I'm going to speak about today is "Do Corporations Ceed a Curpose?" So this is a very topical issue, right? Nowadays, companies are falling over themselves to claim how purposeful they are, to claim that they have a purpose beyond just profit. But there is a backlash. So, particularly in the United States, people are worried that purpose is a distraction. Isn't the goal of a business just to make as much money as possible? So what I want to do today is take a step back and ask, well, is there a reason for a purpose to begin with? And if there is, well, what are the best ways that a company can think about purpose to avoid the controversy that we see in the United States.

Okay? So in order to ask the question, Does a Company need a purpose? Well, let's look at what it would be to have a world without purpose. And it seems that a world without purpose is a world that nobody would ever want to live in.

So often when people say, well, what would a word without purpose look like? They go back to Milton Friedman's famous 1970 article where he claimed that the social responsibility of business is to increase profits. If we didn't have purpose, companies would try to make as much money as possible. And what does it mean to try to make as much money as possible? It means focusing totally on making money and forgetting about any concerns for employees, customers, or society.

So if that's what a world without purpose would look like, it's no surprise that people think companies should have a purpose.

But there's a problem with this, which is that Milton Friedman never actually said that. So this was based on an article in Forbes by Steve Denning, who claims this is what Milton Friedman wrote. And it's not just Steve Denning. You can look at tons and tons of articles about Milton Friedman who claimed, and these articles claim that he argued that companies should exploit society.

But if you read Friedman's article, it's actually much more nuanced than that. So what Friedman argued was that it is okay for a company to focus on profits because as long as you define these profits as long-run profits, if a company focuses on long-run profits, it has to serve society. For example, a company would want to invest in its community. It would want to improve its government.

Why? Because if it invests in its community, then it will be able to attract employees and reduce the wage bill. So under Friedman's argument, it is fine to just focus on profits, because if you focus on profits, that will make you do things that are valuable to society. And so this is the Adam Smith idea that if we're just selfish, if we follow market signals, that will drive us to do what society wants.

So let's look at an example. So, if you were a car company, would you build electric cars? And the answer is yes.

Even if I did not care at all about climate change, I would still build electric cars because there's money to be made. All I need to do is to care about money. And as long as I have a long term perspective, when I think about money that will drive me to investing in electric cars, I don't need any concern for the environment.

So this is a really interesting position. Maybe the purpose of a company should just be about profit, it should just be about shareholder value. And you might think, well, that's a crazy thing for me to say in 2023, because isn't shareholder value a bad thing, right? So if you looked at the European Commission's recent report, they said, well, shareholder value is short term.

If companies focus on shareholder value, they will be short term. They won't build electric cars. But what we teach in Finance 101 is that shareholder value is an inherently long-term concept. Shareholder value is the present value of all future cash flows. And the most important thing in this equation is this infinity here. So the shareholder value of a company depends on all the cash flows of a company until the end of time. And this is not just true in an equation. This is true in reality. So if you look at companies like Tesla, going back to my electric car example, why is Tesla worth so much money today? Not because of its short-term prospects, but because of its long-term opportunities. And therefore, under this view, it's actually fine to just focus on shareholder value.

So that's the case. Why is it that there is this purpose movement? Why is it that I wrote this book "Grow the Pie", about the value of purpose, when it seems that shareholder value is actually much more nuanced than one might think? So I think there's two reasons why a company might actually want to have a purpose beyond shareholder value.

The first is that what I've assumed here is that if a company looks at long-term profits, then it will serve society, because most things a company does to help society will show up in terms of long-term profits. If you treat your employees well, they'll be productive. If you provide great things for customers, they all buy. But a key economic principle is this idea of externalities. It may be that even in the long term, certain things that you do to affect society won't affect profits. And so this is what many people think is the climate crisis. We don't yet have a fair carbon tax, and so companies can just go and pollute, and if they pollute, they're not suffering the consequences. However, a counterargument is, well, companies shouldn't deal with externalities. It should be the government who deals with externalities. Why?

The government is democratically elected and they represent the citizens of a country. It is true that we care about climate change, but we also care about employment. And maybe if fossil fuels were to disappear, a lot of people would be out of a job.

So one argument that people make is, well, when the government sets laws, they decide to maybe tax cigarettes, but not ban them to tax alcohol. They will take into account different positive and negative externalities. The fact that they don't outright ban certain things like fossil fuels means that in the eyes of the government, actually fossil fuels and alcohol are fine as long as you pay your taxes.

To this idea of externalities, it's something which is a bit more complicated because yes, externalities exist, but it might be the government's role to address them, not the company's role.

So instead, what I'm going to focus about is "The Business Case for Purpose".

Why it's actually good for business, not just good for society, for companies to have a purpose. And why am I focusing on the business case? It's not that I don't care about society, it's not that I don't care about externalities, but externalities are always a value judgment. There might be certain people who think climate change is more important, other people think, well, we should actually allow developing countries to industrialize. And it's not me for me to say whether one view is right or wrong, because that's a moral issue. Instead, what I'm going to look at is the financial case. Why might it be better for a company to focus on purpose than even long-term profit in terms of a strategy to make money?

I think the best way to illustrate my view is to give you an example. Let's look at a case study.

So, 20 years ago, Vodafone, which is a UK telecoms company, they noticed that in Kenya, kenyan citizens were using their phones to transfer mobile minutes to each other as a form of currency. And this gave them an idea. They asked, well, what if we could develop a technology that would allow people not to transfer mobile minutes, but actual money? At the time, 15 million Kenyan citizens were unbanked. They had to rely on cash which could be forged or lost or stolen.

So if Vodafone could pull it off, the social impact would be huge. And indeed, four years later, they launched M-pesa, this mobile money service in Kenya, which would allow people to transfer money from phone to phone without even having a bank account.

So you could send money just as easily as you could send a text message. Now, the social impact of this was huge. So within the first seven years of launching M-pesa, they lifted 200,000 households out of poverty, and many of these households were headed by women. It allowed them to move from agriculture to business and retail. So there was a large effect on gender equality. But the nice coder to the story was Vodafone ended up being able to make money from this. Why? Because if they were able to charge a small percentage of every transaction so this idea of launching M-peza, that was driven by purpose, that was driven by the desire to solve this social problem of financial inclusion in Kenya. But ultimately, even though it was driven by purpose, not by profit, it was able to turn it into a profit. Why? If you create value for society, ultimately somebody will be willing to pay you for that value. And so purpose might be a way of inspiring you to make some investments, to make some innovations that you might not have done otherwise. And so this is the framework behind the grow the pie book. So what is the pie to begin with, you can think about the pie as being the value that a company creates. And it can be divided between profits to investors, which is the blue, and value to society that's the orange it could be fair taxes, fair wages and fair prices.

And often, if you're a manager without a purpose, you think, well, how do I maximize profit? I'm going to split the pie differently. I might take from my workers by paying them lower wages or working them harder. I might take from customers by charging higher fees or making the fees nontransparent. And you think you're maximizing profits, but actually, in the long term, you shrink the pie because those stakeholders will walk away, employees will be demotivated or customers will switch to arrival.

So the idea of purpose is that if a company is driven by the idea of creating value, of trying to increase the orange, that will inspire it to innovate in ways that it wouldn't have done otherwise. Right so this is what inspired Vodafone to launch this crazy idea of M-pesa, was the desire to solve this problem of 15 million Kenyan citizens being unbanked. And this did increase the orange. But as a byproduct of increasing the orange, you were able to generate profits and also increase the blue.

Now, you might be skeptical about this you might think, well, if I'm defining purpose as being about innovation, do you need a purpose to innovate? Isn't the profit motive enough? Let me go back to my electric car company example. Won't just a company who cares about profits invent electric cars. And I would say they would do for electric cars, because for electric cars. Yes, the economic case is currently clear, but I would say that many decisions are not like the electric car example.

They're instead like the M-pesa example. Because back in 2003, there was no financial case to develop M-pesa.

Vodafone's business strategy was to expand in the west. It was to inspect from licensed auctions in the west, because the west was where there was most money to be made. It would be crazy to launch this completely new idea in Kenya. First, it would probably cost them a lot of money to launch M-peza.

It probably wouldn't work then. Even if it does work, it might be that this ends up, say, leading to money laundering, bad for your reputation and even if you could overcome that where was the money to be made?

By serving some of the poorest people in the world. So this is why I, despite being a finance professor and an ex-investment banker, can be so positive about purpose, it's for the business case, not just the social case. If purpose inspires you to create value for society, to solve some of the most challenging problems in the world because of the information to address those issues that will make a company go above and beyond and launch some innovations that it might not have done.

Okay, so purpose inspires us to make some investments that could not have been justified by a financial and spreadsheet calculation. And if you apply that to your own life, let's just step away from business. What is the meaning of life? So let's say you think the meaning of life is happiness. Well, if you pursued happiness directly, if I think, well, what can I do to make myself happy? I might think, let me eat a lot of cake, let me drink a lot of alcohol, let me take some illegal drugs, that is the way to pursue happiness directly. But in the long term, you actually won't be happy. But if instead I think, well, my purpose is to be a good professor, to be a good colleague, to be a good friend and husband and father, then ultimately I will become happy as a byproduct. So just like you don't pursue happiness directly, that's the same with purpose.

Okay, so at this point in my talk, more than halfway in, you might think, well, everything Alex says, that sounds great, but where is the evidence? I claim that if companies think about serving wider society, then they will be profitable.

But that seems almost too good to be true. Where is the evidence? I've given you one example of one company where it works. But how do you know that I didn't just handpick this one company because it supports my viewpoint.

So it's great to partner with a research universe like EDHEC, where we want to make sure that before teaching something to students, it's based on rigorous research. So what does the research say about whether purpose pays off? Well, the big challenge is how do we measure whether a company is purposeful to begin with?

So you might think, well, should we look at how much money a company spends on purpose? So how much do you donate to charity? But that's problematic because it could be you donate to charity, but you don't give to the right charities. Or maybe you do some great things to serve society, but it doesn't cost you much. So let's say you're a great boss at work who treats the employees really well. That doesn't cost her anything, but it makes a big difference.

So I didn't want to look at the input, how much you spend, but the output, how much value you deliver. And so what I looked at was the list of the hundred best companies to work for in America. So these are companies that go above and beyond in how they treat their employees.

So you might think, well, why is that my definition of purpose? Shouldn't I be focusing on climate? The climate crisis is so big. Well, climate is very important, but climate matters for some companies, but maybe not for others. So it matters if you're energy, but if you're in tech, climate is not so key.

But every company employees are your most important asset and a major way in which companies affect society. So what I looked at was the 100 best companies to work for. These are companies that go above and beyond in how they treat their employees. And what I found was that over a 28-year period from 1984 to 2011, these companies delivered shareholder returns that beat their peers by 2.3 to 3.8% per year, which, when you compound it, it's 89% to 184%. So companies that are treating their work as well, they weren't just being fluffy. They weren't just giving the pie to their employees. They were growing the pie they were investing in the most important asset. And eventually, in the long term, they became more profitable.

And importantly, notice that this time period included things like the financial crisis and september 11, the collapse of the Internet bubble. So this is something that paid off in bad times, not just in good times.

Well, you might think, well, this stopped in 2011. Is this still relevant today? What's happened in the ten or so years since then? Well, this is why it's great to give a talk at EDHEC, because Professor Hamid Boustanifar and his former PhD student Young Dae Kang, they have updated the paper. And this is not just an update in terms of more data. They also updated it with more sophisticated methodology because there's new asset pricing models that came out after my paper was published. So they used the most state of the art techniques. And what they found was, this continues to hold in the ten or so years since I finished my paper.

Now, the smart among you will think, well, is this correlation or is this causation? Is it employee satisfaction leads to better financial performance?

Or is it the converse? Is it once you're already performing well, then you can start spending money on your employees? Or maybe there's third factors that affect both. If you're in the tech industry, your employees are happy because the work is innovative. And if you're in the tech industry, the company does well because this is a growing sector. And so Hamid and his co-author Young Dae and I, we all did things to address those concerns. Let me not bore you with how we go about doing this, because I'd like to focus on the practical implications, how we could put purpose into practice and then go to the Q/A and the discussion.

So let me just drill down a little bit in terms of what purpose actually means, because of how many companies put purpose into practice is actually perhaps not the best way of going about it. So I see Zoe Decker's asked a question, does purpose have disadvantages for companies? And the answer is actually yes, if you don't go about it the right way. So Zoe posted a question in the Q/A obviously, others please feel free to do so as well or address them as I go along or at the end. So what is the main disadvantage of purpose is if companies pursue purpose in an unfocused way. So many companies have a purpose statement like this our purpose is to serve customers, work as suppliers, the environment and communities, and investors. So they try to be all things to all people. They try to serve everybody. Now, that sounds great, but that's something that you will never be able to put into practice because there's a trade off. So let's take a French example.

So ENGIE, the French energy company, they closed down Hazelwood, the most polluting plant in the OCD, and that was good for the environment, but it was bad for workers. So 750 workers, they lost their job. So you can't serve both the environment and workers because there might sometimes be trade-offs. And if you think about what does the word purpose mean in the English language, it doesn't mean being all things to all people, it means being focused and targeted.

So if I do something on purpose, I do it deliberately. If I have a purposeful meeting, it has a clear agenda. So I define purpose as why a company exists, who it serves, its reason for being, and the role that it plays in the world.

And that might seem a bit lofty and idealistic, but what this highlights is that purpose involves focusing on a couple of things and doing them really well. So your purpose should not be to try to solve all 17 Sustainable Development Goals, but to concentrate on the 2 or 3 where you can really move the needle.

So how does a company decide what to focus on? If there's only one thing you remember from my talk, it is this.

So ask yourself the question, what is in my hand? What are the resources? What is the expertise that my company has? And how can I use this to serve society if I think a little bit more creatively?

So with Vodafone, what was in their hand was their telecoms expertise. And then they use that to develop mobile money, because that was something linked to this core competency.

And notice this is quite different from how other companies think about Purpose, which is often responding externally to what they see in the news, rather than thinking internally, what is in my hand? So, after George Floyd was murdered, many companies started donating money to Black Lives Matter. Now, certainly as an ethnic minority, I care about racial equality, but if you're a company, you don't have expertise to know whether Black Lives Matter is a better charity than Médecin Sans Frontières, or was that a better charity than the American Cancer Society? So if your expertise is not discerning which charities are most worthy, it's not clear to me that giving to charity is the best use of a company's resources compared to investments like M-pesa.

So let me close with 3 ideas on how to put this into practice.

So the first is use what is in your hand to do new things. So to generate new business, generate new ideas. This could be producing new products and services like M-peza, or it could be finding new clients. Now, again, you might think, well, is this purpose?

Shouldn't any company want to generate new business? Even if you only cared about money, wouldn't you want to generate new products and to find new clients? Well, the answer is yes. But you would only do that innovation if you knew that it was going to be profitable. Where the purposeful company will try to find new clients, even if the profit motive is not clear.

For example, there are some banks which have a target of funding minority entrepreneurs. So why is that so important for society? Well, when you lend to a startup business, you don't have things like lots of financial records to evaluate the credit worthiness of the business.

You will often go with things such as the quality of the entrepreneur. And white male entrepreneurs will tend to be more confident and convincing and therefore naturally more money might go to white male entrepreneurs. But a personal bank might think no, we want to provide access to finance to any good person who wants to start a business. So they might have targets of funding female or minority entrepreneurs or even white male entrepreneurs who don't have a university education or in the UK who are from the north of England because we have a lot of regional inequality.

And so that's something which is driven by the idea of serving society. But ultimately, if you serve society, that might turn into a profit because that will allow you to notice clients that you might have done otherwise. If you realize that there are other factors and hidden biases that might cause you to ignore those types of clients, this might actually allow you to write business that you might not have done otherwise.

So the second is doing the same thing, but doing it in a different way. And let me focus on the second point here, which is about the power of excellence. So often people think that in order to be purposeful, I need to do some new activity that which is explicitly with a purpose label. I need to give money to charity, I need to start employee volunteering programs, I need to build a fire station in my local community. But in fact, for many companies, just by being excellent in the core business this has a huge effect on society.

For example, I have done some work with Network Rail. So they run the rail tracks in the UK, a bit like SNCF in France, and they want to do things like reduce the amount of graffiti on the rail tracks. Now, that is really important but the most important thing I think a train company can do is make sure they run the trains on time.

If you run the trains on time, that has a huge effect on society, because people can get to the jobs, people can see their friends and their family. So often just being excellent in your core business has a huge effect.

So again, you might be skeptical and you might think, shouldn't anybody try to be excellent because you'll make money because of it? But a purposeful company will be excellent even in ways that are not clearly profitable. For example, as business school professors, you typically get promoted, you typically get reputation from research, not from teaching.

But a purposeful professor will care deeply about teaching just because their purpose is the dissemination and creation of knowledge. And therefore, this is something that you might do even if there are not explicit career incentives. Just because you love it, just because you care about your students. And the final thing is to do the same thing in the same way, but to recognize the purpose of what you're doing.

So some of you will know this graphic where there's 3 people doing the same thing. One says, I am laying bricks. The second says, I'm making a living. The third says, I'm building a cathedral. So they're all doing the same job but they're looking at it differently.

And so what that means is that you might be able to just do the same thing, but recognize the higher purpose of your activity.

So go back to my banking example. So one bank I know well in the UK is NatWest. And after the pandemic was over, the CEO of NatWest, Alison Rose, she took her senior management team out to a restaurant in Covent Garden called the Darjeeling Express. And the owner of the restaurant, Asma Khan, gave a little talk. She said, When I came to the uK 30 years ago, nobody believed in me. I was a woman. I was an immigrant. Immigrant women just did not start businesses.

But NatWest, you believed in me. You gave me this loan. And you know, I have served this many thousand customers, I've employed this many hundred staff this has been the huge impact of your loan.

Now, let's say that you were the natWest loan officer and you made that loan approval.

Well, you probably didn't think much of it, right? You've given some loans to examine, some go in the accept basket, other goes in the reject basket. You don't really care. But if you are somebody who sees, well, actually, if I made this loan, I will ultimately allow an entrepreneur to fulfill her dreams. That's something that you can see the ultimate purpose. This is similar to how us as finance professors, we love it when students say, hey, we managed to get a job at this company. Hope this was in part helped by your teaching. That's something which gives us huge gratification. But you might think the message I just gave you is something as a senior you can do.

Yes. So as a CEO of a company, you can tell the junior staff as to what was the impact of what they did. What if you start off as a junior person? And that was how I started at Morgan Stanley. I started right at the bottom in a position called analyst. I thought, I have nothing in my hand. I am powerless nobody works for me.

But I realized people did work for me. There was the It department, there was the secretary. And perhaps the most abused department in a bank is called Graphics. So you do some unintelligible scribble and this gets converted into some beautiful slides. And often analysts would shout at graphics for not doing what you wanted, even though it was your fault for not explaining it clearly enough. So when I got good work back from Graphics, I would call them up and just say, thank you. This was a great job. And honestly, I did not do this to be seen as a nice person.

I did it just because I was genuinely grateful, but because I was so junior, I didn't have my own office. I sat on the open plan floor it's called the bullpen. So when I say thank you, other analysts heard me and they started saying thank you themselves. I'm not going to claim that we changed the entire culture of the entire investment bank, but on that one floor of that one office in Canary Wharf, London, people started treating each other a bit more kindly. So you often think that you are a thermometer. A thermometer reflects the temperature around you.

If the temperature is really cutthroat, you need to be cutthroat to survive. However, you can be a thermostat, and a thermostat affects the temperature around you. And even if you're the most junior person in an organization like me, as an analyst in this big investment bank.

We can start to play the thermostat.

Okay, so that's the end of my talk. So, as Laurent kindly mentioned, I wrote this book about purpose called Grow the Pie, which was recently translated into French. And why I wrote it is, I think for too long people thought about purpose as being nice and fluffy, but not something that serious business people should care about because it's at the expense of profit. What I wanted to highlight is that it can be fully consistent with profit, but there are downsides. So I really like Zoe's question about the disadvantages is that if you pursue purpose, you need to do so in a focused and targeted way.

And so just before I hand over, let me just answer Nabia's question who asked, what is the difference between vision, mission? And so? To me, they're all pretty much the same thing. So there are some organizations who will say, our mission is this, our vision is this, and our purpose is this. And they'll call them slightly different things, but to me, it doesn't really matter what you call it. What matters is do you actually live it? So I think all of these things are reasons for why a company exists beyond profit, you could call it vision, you could call it mission, you could call it purpose, you could call it ethos. But if you're inspired by doing something above and beyond just making money, that will ultimately lead you to being more successful, because that will free you, that will inspire you to take decisions in accordance with your vision or mission or purpose or whatever you call it, which you could not have justified with purely a financial calculation.

So thank you so much to everybody again for inviting me for attending this, we've already had some two great questions, but I think we're now going to have some moderated Q&A. And if there's further questions, do please post them.

Laurent
I do hope so as well, Alex and before I ask Hamid to say a few words, I just wanted to get back to Zoe's question about negative perspective on purpose or negative, kind of examples. So if I hear you well, I say, okay, now if you're a purposeful company and you do it the right way, it's necessarily going to be positive. And I had the feeling that, yeah, this is very good and all those counter-examples would be easy to say, well, they made it the wrong way and so this is the reason why it's negative. Do you have anything to say about that?

Alex
Yes, I think when companies stray away from their comparative advantage, from what's in the hand, I think they get into dangerous territory. So sometimes companies are playing the role of government. So you have certain cases where there were some investors in the UK criticizing, like the Chinese government or the Hong Kong government, some companies speaking out about the Florida don't say gay law and so on. And I think the problem with that is that as a company, that is not your prerogative, that's not your responsibility. It could well be that there's different citizens with different views on these issues, and you will alienate many companies, many customers. You will alienate a lot of employees for that. I do think you should serve society, but you should serve society in things which are using your particular expertise. So this is why, when I give some YouTube talks, they will be on things like basic financial literacy, teaching people what is simple interest and compound interest. I will not speak out on things like abortion because people will listen to what I say, because they will respect a professor, even though I have no expertise in that issue and I would stay away from things which I don't have expertise on and I think that should be the same for companies.

Laurent
Okay, thanks a lot, Alex. Hamid, I think this is your turn now.

Hamid Boustanifar
Thank you very much, Alex. Yeah, so I find it very interesting the idea that if you go after purpose, ultimately in the long run you may serve obviously your shareholders as wellvand I just wonder, if that's the case, why are there so many companies that they do not have a purpose? Or maybe you disagree that you may say that most of companies they have a really clear and purpose that helps the society, but I believe that's not the case. We see frequently and often the companies that you made the example of actually employees, that they mistreat their employees, all those kind of things. So what drives you think those decisions. Or you are muted?

Alex
It's a great question, Hamid, because people might think, well, everything I said sounds logical, so why don't companies actually do it? I think there's two reasons. I think one of them is the idea of a pie-splitting mindset, that the pie is fixed. That is something which is really ingrained in us. We think that life is a zero-sum game and the only way to win is to make others lose. So it's just instinctive that if I want to increase my blue, let me take from the orange. And so like the games that we pay when we're children, they are zero sum games. We win by making others lose. You win the game of Monopoly by having a house and charging your appointment, your opponent, and so the opponent goes bankrupt. We want to beat other people. We typically live in zero sum games. But I think also part of the reason is that this purpose narrative has sadly been entwined with politics. So in particular in the US. People think, well, if you're a Democrat, you should support purpose and if you're a Republican you should oppose it. And I do have to say that I think it is some purpose people who are fault for that.

So they will say, oh, you are a stupid Republican, you are a climate change denier. And so if they give the impression that Republicans don't believe in purpose, then if you are a Republican, you might think, well, I shouldn't believe it because this has now become a political issue. So this is why whenever I talk about purpose, I never talk about politics. I say Purpose is just good for business. And if something is good for business, republicans should absolutely embrace it. But I feel the politicization where Democrats have finger pointed about Purpose and said, oh, you don't care about your employees, you don't care about the environment. That's made Republicans think, well, to be a true Republican, I shouldn't care and I should just focus on profit.

Hamid
Thank you. So could it be that the incentives are also not set right in a way that a typical CEO has a tenure of, I think five years or six years? And how, if you are the CEO of the company, you know you are not going to be there after a few years, your decisions would be very different, right, than if you knew that you are going to be there all your life?

Alex
I think that's also another good so that's the most rational reason, I think, why companies might not be purposeful is yes, in the long term, the pie can shrink or grow, but that's in the long term. In the short term, the easiest way for you to increase profit is to split the pie differently. If I charge higher prices or pay lower wages, that will immediately hit my bottom line. Yes, in the long term, the pie will shrink. But if I'm only going to be around for another year, I don't care about this. And similarly, you might think, well, if I want to increase my profits, don't I want to grow the pie? But it takes a long time to do that. And so when you have short term incentives, I think then you will not have incentives to be purposeful. Now, that is changing to some degree. So a lot of my other work is on executive pay. And what we see recently is a lengthening of executive horizons. And importantly, this lengthening extends beyond the tenure of a CEO. For example, there are now some CEOs who, after they leave, they still have to own shares in their company.
And I think this is a positive development because this means that their horizon is longer than their tenure. But absolutely, your point is spot on, Hamid. In order to encourage companies to think about this more, first, we need to get rid of the zero sum mindset. Second, we need to depoliticize the issue. And third, we need to give long term horizons. Because if you're focused on the short term, the best way to increase profits is to split the pie and not to grow it.

Hamid
Thank you, sir. I would ask one more question and then hand it over. And then maybe I get back to the rest of my questions. So nowadays it's been a while, few months I've been working on sinful companies. So those companies that sometimes is called irresponsible companies as well. And I read a lot of companies filings out of my whenever I have time, I just spend my free time on reading companies findings. And then, as mentioned, most of companies, they have statements that our mission is this, our vision is this, the purpose is that. And then I was reading, I was trying to see what do the companies in the defense sector, they would say? I thought that if they want to mention what's my purpose, maybe we can make the society safer, peaceful, by giving guns to people and so on. So I don't know, I was just wondering. I read two of the companies, the famous one, Lockheed Martin and also Raytheon Technologies. None of them, I could find any sort of mission vision or statements. That's the way we serve the society. I wonder, what do you think of this company, in the sense that we have evidence that these companies also outperform, at least there are some evidence that they outperform the other sectors and the peers and so on.

So what's your thoughts on these companies?

Laurent
If I can rephrase is the question, do sinful companies has purpose as well? That could be a little bit like that, no?

Alex
Yes, I think there's two sub-questions. So what Hamid mentions is quite interesting. I think there's two issues behind this and like Laurent, you highlighted one. So one issue is first, do you need to have a stated purpose? So Lockheed Martin and Bacon, they don't say anything. I actually don't think you need to say something explicitly. So I think there's too many companies who focus too much on the explicit statement of purpose and not the actual delivery. So I don't know how much coverage this got in France, but in the UK there's this big controversy where Unilever, which is seen to be a really purposeful company, they were under attack by Terry Smith, a legendary investor, and in particular a long term investor who says Unilever is so obsessed with purpose. They're trying to define the purpose of mayonnaise. But the purpose of mayonnaise, they said, is salads and sandwiches. Right. Why do you want to define a higher purpose than this? If you look at Ben and Jerry's purpose, they say, we believe ice cream can change the world. Really?

Right. People like ice cream, but can it change the world? It's not like a cancer cure or something like this. And there was one of my friends, my former student is a portfolio manager in Frontier Markets. So these are not emerging markets. These are even more emerging than those. And many companies there, they don't have the statements, but they just act purposely. They're not concerned with reporting or showing off, they just do it. So I think to one part of Hamid's question is I don't think you need to stay superb as what matters is what you actually do, but then what you actually do get to Laurent's. Question is, if you're a sin company, you do sinful stuff, can you actually be purposeful? Why is it that you outperform?

And so my view is that I think this idea of sin or exclusion, at least as some asset managers practice, is a bit black or white. So they define industries as sin. And then they often have to reverse and do a U-turn on the decision. Right? They define defense as sin. And then when Russia invaded Ukraine, they suddenly said, oh hey, a defense is actually good. And for me, I would say things are not so black and white. So I've served for seven years on the investment committee for Royal London Asset Management. So we have about 15 billion pounds of sustainable funds and we don't exclude alcohol. Why? Because we actually think that alcohol could have a positive role. So in a society which is increasingly fragmented, where people might sort of feel lonely or just have only sort of exist on online relationships. That's something that can bring people together. If you go to a finance academic conference, it does at the end of a long day of papers and nasty discussions, it's nice to have a drink with people and those who are studying at EDHEC Business School right, there's hopefully some meaningful relationships that come between students which are facilitated by this.

Obviously, yes, there are negative aspects of alcohol, but there's negative aspects of chocolate, of ice cream, of cars, of tech companies, there could be addiction and so on. So I like not to define things as black or white. If you're an alcohol company and you are truly committed to responsible drinking, then I think that's something which could potentially be purposeful. Now, there are certain industries that we completely will never invest in, like tobacco, because at Royal London we believe that that's something where society doesn't need it. There's not the same social aspect as there is with alcohol, but the list of industries we completely exclude is probably quite low compared to most other investors because we like to think that these things are not so black and white. There could be a need for a good responsible alcohol company. Would the world be better off without alcohol, with prohibition, we would say no, thanks a lot.

Laurent
So our beloved students now, Marine, maybe you can start and then ping her that you will follow up.

Marine Kazarola
Yes, sure. Thank you for your presentation. I was wondering, how do you know that you chose the right purpose? And is it just a matter of competitive advantage or are there other criteria to take into account?

Alex
Yes, I think competitive advantage is one of the main things. It should be linked to what you are good at. And this is why I think purpose is different from CSR. So CSR or corporate social responsibility, this is often divorced from the core business that could be your core business is I'm going to make money with tobacco and then I'm going to give some money to a lung cancer charity. But that's nothing connected to the core business. So purpose is about is the way that you generate your core business, one that creates value to wider society. And so that's why I think it should be based on competitive advantage. Another thing which I think it should be based on is materiality. So there's lots of stakeholders that you might care about, customers, workers, the environment and so on. But some of those customers, some of those stakeholders might be more important to others. So I know, for example, Olam, that's a Singaporean agribusiness, to them, suppliers are really important because you do need sustainable farming, a sustainable supply chain. But if you're say a plastics company where your main input is petrochemicals, that's not something where you need a strong supply chain relationship because your supplies are a bit more commoditized.

Marine
Okay, thank you. And maybe one last question. Who decides the purpose of a company?

Alex
Well, it should be everybody, and that seems a bit of a cop-out, but I think it should be because often we think that it's the management in some off-site with flip charts and marker pens suddenly decided on the purpose and then asked everybody else to implement it. But this, I don't think, is what happens and if it happens that way, then the purpose could be something which is rather alienating to the rest of the companies. So many companies that I know of, they will try to seek all the company's input, so they will have some brainstorming sessions and some panel sessions where they'll try to get input from all employees at all levels as to what the company should be. One of the cave, I'll say is that a purpose will be general, particularly in a large company, and it might not be particularly applicable to every unit within the company. So there are some companies with rather vague purposes. So with Lloyd it's helping Britain prosper. Well, that's vague, right?

Any company is about helping you prosper. If you're a pharmaceuticals company, you do that, but that purpose at a macro level needs to be sufficiently broad that every unit within that Lloyds can subscribe to that, and then a unit within that might have your own specific micro purpose which plays into that macro purpose but is more targeted for you. So Lloyds's Investment banking department might look at that differently from their retail banking department. And so you're never going to run a company with one sentence. So instead what you would have is sort of a broader purpose. Just make sure people are all moving in the same direction, but micro purposes of individual business units which sort of feed into that.

Marine
Okay, thank you. Maybe Ming. You have any questions?

Ming Hug Singh
Thank you, professor, for your presentation. So my question is, I think there are some companies indeed purposeless or have very little purpose, but yet they manage to let the general public or the stakeholders to perceive that they have a very ambitious purpose. So I wonder if it is necessarily a bad thing and how regulators attitudes on it on this issue? Thank you.

Alex
Thanks for the question. Ming, could you give an example of such a company? So what are you thinking of in a company which is purposeless but is able to convince people otherwise?

Ming
Well, I think, for instance, maybe Apple could be an example who haven't disclosed very clear stated purpose, but yet they managed to make people believe they're like changing the world and et cetera.

Alex
I believe Apple is extremely purpose so. I think what matters is not whether you state your purpose, but what you actually deliver. For example, with my Apple iPhone, there's so many things that I can do. So if you give a phone to somebody in a developing country, you completely change their life. And so with me, well, what can my phone do? It just allows me to if I was to go out on the street with it, then I can easily use Google Maps on my phone to get to places, I can do banking on the phone, I can do shopping on this, you can track your health. So there's lots of, I think, possibilities that from that. So I think they're actually very purposeful companies. So in chapter two of the book, I go through Apple, I even go through things like their tax position. I go through how, if you have an old iPhone, they have this robot called Daisy, which disassembles the iPhone into its components and recycles them. So I do think they're very purposeful. And I think your point is an important one, because there are many companies where the public will think that they are not purposeful, but not actually understanding what they do. So often people think banks are not purposeful like, if you're a food company, you make food, if you're a clothes company, you make clothes. But what does a bank do? It doesn't make anything. But I think banks are hugely purposeful, because without mortgages, I would not be able to live in the house that I'm in now. Without mortgages, without a loan, the Darjeeling Express and Covent Garden would not have been able to open investment banks. They're seen as even more evil. But I used to work in this industry and you were advising companies on their biggest problems. So I think society has too black and white a picture about what it means to be purposeful. And this is linked to the early question about sin. I do think alcohol companies can be quite purposeful. Now, let me just try and loop in Zoe's other question about purpose in china and international business, and this is again, another great question. Often people think, oh, well, Chinese companies are not purposeful, let's look at what they're doing. But we need to understand that purpose means addressing the social challenges of a particular country. And the challenges in China might be different from in France or the UK. So at Royal London, we looked recently at a Chinese pork producer, and the animal welfare standards of this company were lower than what you would expect in the UK.

Alex
But in China, there's this big problem of malnutrition, where you have a huge population who might just be eating rice, they might not have an affordable source of protein. So we actually thought that actually, while we do care about animal rights, it was more important to provide an affordable source of protein to many hungry people. So, from a Western standpoint, you might say, oh, this company is not purposeful because it doesn't treat its animals well. But we would say, well, actually, just given these challenges that China faces in terms of nutrition and the source of protein, actually this could be seen as purposeful right now.

Laurent
Yeah, but to move on to him question and possibly related to this one, what about the companies who are faking the purpose? So we can talk about greenwashing or these kind of things? In a sense, you can think of these companies who say they have a purpose, but they don't follow this purpose. Really. So I guess your answer is going to be, well, what do they do? And really they do. But what about the regulator with respect to the fake purpose in a sense?

Alex
Yeah. So I think that there are companies which are not purposeful. I fully agree with you, Laurent, but that to me is not so much a relation of what they're saying. All I care about is what they do. And if you are, say, an investment bank who encourages your clients to do M&A deals knowing that they are buying overvalued companies, then this is absolutely not purposeful. If you are, let's say, a business school and you release research which is fraudulent, or you teach stuff to students which is not actually backed up by research, I think that is problematic. Unfortunately, a lot of these things are quite difficult to regulate because how do you tell that an investment bank has advised on a bad merger? Because often it will take five years before you see the long term consequences of a merger. So we often would love regulators to step in. And often when there's a scandal happening, we say we need more regulation. I certainly think we should regulate where we can. But I also think we need to be realistic and realize that regulation won't solve every single problem that we have in the world.

Laurent
Okay, Hamid.

Hamid
Yes, one last question. We are supposed to finish at 7, right? Yeah, the last one. What about teaching our students? So I think, as far as I am aware of, we still teach our students NPV maximization and all those things. And given that you are the author of Now Principles of Corporate Finance, I wonder if there is something is going to be there that we teach these, people to think of purpose as a fundamental part of a business?

Alex
Thank you, Hamid and so this is a nice ending because this links to a very recent article I read called Applying Economics Not Gut Feel to ESG. While that is an academic paper, it's written for practitioners and in particular about teaching sort of ESG in finance. And what this highlights is that actually traditional tried and tested methods, they still work for ESG. So if you go back to that equation that I had earlier so a shareholder value NPV, that takes into account the long term consequences of a company's action. So actually, people like to bash net present value. But if you practice it correctly, you realize that net present value includes all future cash flows. So I think the problem is the opposite. There's people who claim our current principles in finance are not applicable to ESG. Let's invent something new and make it up and apply gut feel when actually if you understand the principles of finance correctly, they are actually very supportive of net present value of long term investments in things like ESG.

Laurent
OK, thank you very much. Thank you very much. Alex. You really met our expectations that they were really high so I'm not going to say that was above expectations but that was amazing. Thank you Hamid. Thank you Ming. Thank you Marine. Thank you Emilie for organize everything that was extremely insightful and I hope that you all learned a lot about purpose. Next session for the speaker series will take place on Tuesday the 2 may with Ayako Yasuda and she's going to talk about Impact investing so don't hesitate to join us there. Alex, thank you very much.

Alex
Thanks so much Laurent, Hamid, Ming and Marine.  It's great to be here.

Ming
Thank you.

Marine
Thank you very much.

Make an impact
EDHEC BUSINESS SCHOOL
[Music]

https://youtu.be/aA32zl8Z-vw

[Music]
EDHEC Business School
EDHEC Speaker Series - The future of Finance
Estimaring corporate carbon emissions with machine learning

Raul Leote de Carvalho
Deputy head of the quantitative research group
BNP Paribas Asset Management

Tuesday, 4 April 2023
6 P.M - 7 P.M Paris time

Emmanuel Jurczenko
So Raul Leote de Carvalho is actually deputy head of the Quant Research Group at BNP Paribas Asset Management. His team is responsible for supporting research, development of systematic strategies using big data are just to try to support Alpha sources. Raul is very senior, got more than 22 years experience in quantitative investment experience and I discover in fact a few days before that, Raul in fact got another life before quantitative investment. So here the PhD in theoretical physics from University of Bristol. Get MDC in condensed matter physics based in chemistry. So you can see that there are different paths in fact to venture in quantitative finance. And we are very fortunate to have Raul today. Raul is based in Paris and he will talk today about how in fact to estimate non reported corporate carbon emission using machine learning. So, without further ado, let me turning to Raul. Raul, thank you again and the floor is.

Raul Leote de Carvalho
Today. I mean it's really great pleasure to talk to you about the work we have been doing on machine learning for predicting carbon emissions. So we can just go to the next slide where essentially we give the motivation for this work. So, I mean, we are an asset manager. We are a BNP Paribas Asset Management. So why do we spend time constructing machine learning models to estimate carbon emissions? So if we go to the slide, before essentially as an asset manager, fund managers do need to know about company greenhouse gas emissions. These days, I mean, asset managers acknowledge the need to accelerate the transition towards net zero. In fact, there is the net Zero Asset Managers initiative. So this is in slide two. The previous slide, there are 301 asset managers which sign to the Net Zero Asset Manager initiative which is actually with a total of $59 trillion US Dollars, and the assets under managed that will essentially have to converge to zero emissions by 2050, that's the commitment of the asset managers. There's also the Glasgow Financial Alliance for Net zero. It has 550 members from the financial industry from seven specific net zero alliances.

So yes, the financial industry is now committed towards reaching net zero by 2050. And the only way to do it well, you have to know the emissions of the companies because you finance companies, you invest in companies and you do need to know about how much of the emissions are they responsible for. So that's one of the reasons for essentially our strategy and to know how we're going to continue to invest and to finance companies. The second reason is because companies with higher carbon emissions face higher regulatory and legal risks. So I mean, just from a risk management point of view and when we decide to invest in companies, it is important to know how high the carbon emissions are. The other reason is also because increasingly our investors have not just financial utility, but they also have sustainability utility and they want to have a positive impact on environment through their investments. So it's actually our clients that also are asking us to take action when it comes to build portfolios. And in some cases they do want to have very specific constraints related on how they build their portfolios and constraints in particular related to carbon emissions and decarbonization.

So these are some of the main reasons why we need to have carbon emissions from corporates. Now, the issue is, and I will show that in a minute, is that there's not that much published data. In fact, a lot of the data that exists tends to be estimated. And what we found a few years back is that the models that they use by a number of data vendors, perhaps not of the quality we would like to have. So that's why we really embarked on this journey of calculating ourselves to have a tool that will allow us either to have numbers or to actually just check the quality of the data vendor so it can use different ways. And so far what we have done is produced a model for scope 1 and scope 2. So talk about that. So if we now indeed move to the okay, we can stay on this slide. Sorry, we can go back to the slide. So when you talk about carbon emissions, what are we really talking about? Well, first, the greenhouse gas protocol, which was essentially proposed in 2015 and these days is what is mostly used and sees essentially the emission of greenhouse gases as we have in this slide first is not just CO2.

I mean, what it's done is there's a way of converting emissions of other gases like methane and so on into a weight of equivalent CO2. And the way that is done is by well, it's known what is the warming power of different gases. So you can calculate an equivalent of how much weight of those gases in terms of emissions is to 1 ton of CO2. So you have a way of converting emissions of methane and other greenhouse gases into an equivalent weight of emissions of CO2. And that's essentially what is used. So when we talk about carbon emissions, usually you talk about equivalent CO2 emissions, which means all these gases that are here in this slide, they have the weights converted into equivalents of CO2 emissions. Then we have CO2 equivalent. That's what we really talk about. And then I already mentioned scope 1, scope 2, and there's something which is scope 3. So what are these? Essentially, according to this protocol, the carbon emissions and those of all the gases are divided into different scopes. And when we talk about scope one essentially we're talking about the direct emissions from a company that typically come from what is known as stationary combustion which can be burning oil, gas, coal and other things in boilers, furnaces but essentially by the company itself for its operations.

Can be also mobile combustion from the cars and vans and the trucks controlled by the firm. One by the firm and can be process emissions, for example, if it involves chemical production or the equipment that releases CO2 for example cement manufacturing. So this is also included and also includes fugitive emissions which can come from for example leaking in the case of methane, that's really a critical issue. But these are direct emissions from the company itself. Then you have scope 2 and scope 2 is indirect emissions. They typically associated with the electricity that is purchased by the company or steam or heat or cooling but essentially is what is bought indirectly by the company and this is what is scope 1, scope 2 and then there is scope 3. Scope 3 is essentially all the rest. It's also the more complete and more complicated it can also be the bigger, much bigger than scope 1 and scope 2 added together. And scope 3 involves what is known as upstream activities and downstream activities and essentially is everything that is in the supply chain of the company and that is not direct or indirect. So I mean, you can see for example employee commuting is part of scope 3.

You can see other things that are part of scope 3 includes the fuel from related activities, the transportation, distribution, bring materials for essentially the production purchase goods for example, that generate emissions and then downstream is essentially from the products, the emissions from the products or from the distribution of the products from the recycling of products. So all that and there's essentially 15 of those scope three depending on how you really put it together. So that's what we call carbon emissions. Now, if we go to the next slide in slide four, this is the picture on 2018 December. That's because of what we use for the calculations today. There's a few more companies publishing data but what we have in here is the number of companies that provide already-reported data on emissions.

Emmanuel
Raul, if I may, in term of the universe, when you mean broad global universe so what are you talking about?

Raul
Yes, so I mean, if you go through all our funds you find potentially these 15,726 companies at least you would back then when we started the analysis. And so if we want to report on every single company we can buy into our funds, that's our investment universe. So that's what we mean 17,000. Of course, I mean it includes a lot of micro companies and a lot of small companies. You can see there's the 3000 largest cap universe. That's for the largest cap funds, of course you go on a small universe. We also have what is available in Bloomberg where in terms of the universe they consider which is 11,700. So the issue is we potentially can invest in or you could it's probably more even now back then on investing 15,726 companies. And when we look at what we have reported we had only about 18% of scope 1 and about 17% of scope 2 reported and scope 3 even less, only 12%. But even if we go for the large caps of the companies that appear in our mainstream funds, flagship funds, which are invested typically in the largest capitalization so talking about the 3000 largest micro capitalization stocks, you can see that still it's less than, or it was at the time, less than half that were actually reporting.

And on scope 3 it was only 33%. So even for those funds, I mean if you faced with a decision do I want this company or that your criteria is carbon emissions, you can see you really have a problem because you don't have the data for half of your universe. So it's not fair. I mean, you can say well then I don't invest. But if you say I don't invest, you just forget about quite well. Most of 80% of our investment universe is really throwing it in the bin, which also is not something that we want to do. I mean, I'm sure there's good companies that do not have a lot of carbon emissions there. So I mean, this was what we were confronted with and that's why we embarked on this. The second reason we embarked on this is because when we ask some data vendors to extend, because they use models to estimate missing data so they have more coverage, but it's based on their own models. And what we realize is that most of the data vendors, when we talk to them, in particular back then, I mean, this also has been changing, but in particular back then, they had extremely simple models, typically linear models, which based on very few variables, typically size, number of employees and sometimes it just stopped there. And so, I mean, there was this other problem that when we started looking, we realized that the models that some data vendors had were really not very sophisticated and not that powerful and they were far from what we wanted to have because our objective is indeed to have leader on sustainability and sustainable investing. So that's why we embarked on constructing these models. And also back then, I mean, we talked to in particular some academics from the University of Otago at one of the Grassfield conferences and we realized that they were using machine learning. It's based on the discussion that we came up with, this way of modeling. So if we go to the next slide, I just mentioned what some academics were doing back then and in slide 5 we show what we have changed relative to the models that existed previously based on machine learning. When we looked at the paper they presented at the conference and that eventually they published, we realized that, well, we can go to the yes, exactly. So we realized that their effort was on developing essentially one model per industry. And sometimes if there was not enough data, they were kind of putting some industries together in terms of gigs, three gigs, two levels and so on.

So they had a way of putting together industries based on availability of data and what they could do. But they're really aiming at one model per industry. That was their aim. And what we realized is that some industries, for example insurances, the predictions of the models they were putting together out of sample were really poor. And also they were using machine learning models using a methodology which essentially just throw everything that we can from machine learning algorithms and let's really try to pick up the best and construct something which for us was a problem because for us having some transparency of what is going on is also very important. Not just forecasting precise numbers, but knowing why we're precision forecast numbers because we have to explain why we're doing and what we're doing. When you rely on the data vendor, you can say well, rely on the data vendor, the model perhaps is not good, but you just say that if we're going to forecast ourselves, we have to say also why we're forecasting in a particular way and why we have carbon emissions in one way, which was a challenge. So what we opted, it was first we decided why not create a global model and just use sectors as an input.

And we realized that that works really quite well and solves the problems of out of sample predictability in the industries or sectors that previously were not good. Like insurances, we actually can get quite larger squares in the results in the validation in the test sample. So that's what we went for. And then also one of the things that we realized that was extremely important was to correct errors in the reported data that we were using to construct the model. So back when we started this, this was done iteratively I mean, every time we run a version of the model we also detected the companies that were really outside in the test sample, that were really outside where we wanted to be both in the test and validation samples. And we looked at why the data was really far out and we realized that there were errors in reporting and we could do that just by going actually to check what the companies had published. And finally, sometimes the numbers had not been updated, sometimes there were cop productions that had not been taken. And so by correcting those we actually managed also to improve the model.

And in the paper we describe still the fact that we did this by hand. Today we have an automated version for doing this. But that is very important. If you train a model on bad data, you will get not necessarily a good model. If you train the model on good data, of course chances you get a good model are really significantly higher. So that's something we've learned as we went on is that the reported data, as we get it, contains errors. And being able to correct those errors for cop productions, for misreported data and really going and looking at where the data has changed really significantly from one year to the other without any apparent reason, then we decided to just omit some of this data. If we could not find an explanation or correct the data or something. So the main two innovations is indeed by switching to a global model with sectors and industries as predictors and correcting the data, and I would say the simplification of just choosing the machine learning algorithms limiting to just in the case of the paper, two algorithms and in the case of real life now we actually finally focus just on one, I will talk about that.

So this is the first part of the model is that it's a global model and we do the deceptic cleaning and we pay a lot of attention to that. In terms of what goes into the model. We can go to slide 6 and we have essentially a number of predictors. A lot of them were inspired already in what academics were doing. We completed here and there and essentially this is the data set. So, as I mentioned, we have industry classifications, which is a categorical predictor. We have regions, as you have there. We have the revenue group, whether it's a high income or upper middle income, so on also as a categorical predictor, we have whether there's tax regulations or not. And then we go into the numerical data, whether we have the CO2 emissions, we have carbon intensity of energy mix and we have a number of other things related also to carbon emissions. Then we have something specific to company, but not just revenues and number of employees. We also added things like total assets, gross property, capex, age of assets, and finally also we added variables which depends on essentially to related to the energy production and energy consumption.

So this is what we tested. I mean, there were more predictors that were tested in the paper. We really only mentioned what was finally retained for the final version of the model. So this is what we work with. Although, I mean there was actually a few more things that have been tested, but they were not really retained by the model. So we decided just not to talk too much about those. And everything is done in log terms, as you will see, carbon emissions, they can increase quite fast. So we deal with log terms. So for the numerical variables, typically most of them are also in logs. So we regress logs on logs when we construct the models, or we model machine learning in terms of logs, because that gives us is as it was done and as it's done by most people. Because the data, if you look at it just as it is really not Gaussian distributed. But if you look at the logs, then you find something really close to Gaussian distributions. So that's why in these models you model logs.

So in the next slide, we talk a bit more about the machine learning approaches that we have used. So we chose to use a linear model particularly because when we started this, we really wanted to have something that would tell us a bit why the model is doing what it is. And when you have a linear component that becomes easier, at least you can look at what the linear component is doing. And we went for elastic necks. I mean, we could have done many of the others, but elastic necks essentially gives you the compromise between the rich regularization and the last regularization and it creates essentially the best compromise between using both. So that's a kind of standard choice for a linear model. And that's finally what we went on. Now, the predictions are quite easy to explain. The issue is not as accurate as nonlinear models. And for the nonlinear models from all the things that we could use. Finally, we settled for using what is known as Extremely Randomized Trees, which is essentially a version of Random Forests, with a number of simplifications in particular on the way that you do the split, which are random, as not really chose to be optimal. And that makes it more economical in terms of computing time.

So that's what we went for. And indeed, I mean, we find that it tends to be more accurate. But when we actually wrote this first paper, well, the problem is we could not figure out how to actually come up with an explanation of why we were getting the numbers we were getting. Now, in the meantime, we started using something which is known as SHAPley values. So in the meantime, we actually dropped the linear model. We just used a nonlinear regression model and we used SHAPley values to essentially find out why we get a certain prediction from the model. And I will talk that at the end because that's something that we've done just recently. But on this model as it was presented, I mean, also we decided to go for a conservative combination. We could have chose just picked the best model or the best result of the model, but mainly because the regulator also wants carbon estimations in case of uncertainty to be conservative. So actually we not went for the best of the best estimations, we went for a compromise. So essentially we just took what we call the maximum prediction combination, which is we just take the biggest value from the results that we get, which is not necessarily the one that gives going to get the best fit, but it's what we chose and this well can be changed.

And what we do now, we go for the best fit. But when we wrote the model, this paper, that's what we went for. So if we go to the next slide, I think it's interesting to spend a little bit of time on how these things are set up, or how we did set them up. So we first separate the universe into in sample and out of sample. In out of sample, we essentially put every while all the companies not reporting, all the companies for which we realized that the data had problems because it changed too much, or there's something we really couldn't understand. So everything that was excluded was put on the side. And what we did in we're going to show at the end is we compare our estimates for those companies that are excluded because they don't have emissions, or because the emissions, we don't think they are good in terms of what they're reporting. We can compare those predictions with the predictions from different providers. And what is interesting is that some of these companies have been now publishing. So what we're doing now, and we'll talk a bit later, we're now comparing the prediction of the model with actually what has been publishing.

So today the way we judge the model is actually based on the data that companies that didn't predict in the past are predicting now. When we constructed this first version of the model, we just used out of sample to really compare with other data providers. In terms of the construction of the model, we used log-transformed data, as I just mentioned, because it's what it is normally distributed and the predictor is also log-transformed. We used the technique called cross-validation. In the next slide, I will show a bit more what it is. But for cross-validation, what we did, we took the in sample data and we partition it in 80% for training, 10% for validation and 10% for testing. And I will show in the next slide when I get there how this works. But that's essentially what we have done. As I mentioned, we have these Iterative procedures. So once the model was developed, we didn't just stop there, we actually looked at the biggest outliers and checked if there was any reason for having them as outliers. And if we find that there was potentially an error in the data, we just removed them and we reconstructed the model and we found that that indeed increased the model.

So either we correct the data, in particular if we find that there's really something strange going on there, or else if we find that there's an error, then we remove. If we find there's no error, I mean, it's just an outlier, of course we keep the data. But this was investigated and quite often we found that it was the data that was wrong and needed to be corrected. So we corrected that by hand. And finally, in terms of evaluation of the model, I mean, at least for this paper, what we looked at mainly was the R2, just comparing essentially the data points we get with what was reported and also against what is the prediction. So on top, what you get in terms of prediction, and on the bottom, what you get in terms of reported. So by comparing essentially the two, we can calculate the R2 and you do this for the test set. So it's really as it's traditionally done for machine learning models. So in the next slide we just a bit on the cross-validation. So the way it was done, as I mentioned, the data was divided into 80% for creating the model, which is essentially every time you divide the data into five and you keep one data set as the test data and you fine-tune the hyperparameters of essentially your models.

For example, for the extra trees you have to fine-tune the depth of the tree, you have to fine-tune the number of trees and so on and so forth of all these parameters. Essentially you can fine-tune by running this a number of times and running it in this way and you use the test data essentially for that. And then at the end you come up with your model and you have essentially 10% of the data which was used for the final evaluation of the data with data that has not been used to construct the model. And that's where the R2 essentially come from. So that's a typical approach to put together machine learning models using cross-validation. Slide 10, we show what the model has done in sample using these, so what we have here on the left is scope 1 emissions. This chart, the way to see it is we start on the very left with the company with the highest emissions and here we have the reported emissions with looking at the quality of the model. So we have the blue dot right on top with the company with the highest emissions.

And then you just add one company at a time and you go all the way to the company with the lowest emissions. So you have the reported emissions, scope one plotted one after the other for the companies ranked by scope 1 emissions. And the green dots is essentially what the model can achieve compared to the reported emissions. And on the right you have the same principle, the same graph for scope 2. And you can see that towards the less lower emission companies the quality of the fit is a bit less good. But in fact, that's also because we were being conservative and we were opting for the max estimation. Now do we remove that and we get a better fit in the models that are actually used in production, but for the paper and for what we are doing back then, that was the choice. So in fact they are squares also not as good as they could be because we had this conservative approach. Another way of visualizing this data is in slide 11, the next slide. We're just looking at the differences and we can see that the next slide in slide eleven, we can see that they're not exactly Gaussian, as I mentioned, we can see that indeed, we tend to overestimate carbon emissions, but that's also by construction here.

In fact, that has finally, for the very final version of the model, we decided to remove that. So we have distributions which are a lot more Gaussian. But this is how the errors of the model are distributed. And what is also interesting to see is look at the R2 of the model. It's in sample it's for the final evaluation of the model. We have R2, which indeed tend to be quite high. And what is important in particular is that they quite high for the most important sectors, which are the ones which have the highest emissions. Because as you can see also, when it comes to the emissions of sectors. Well, utilities for scope 1, which is the table on the left is the one that is the sector with the highest emissions. That comes materials and energy and transportation. And then you can see that quite fast the emissions fall quite significantly. So what is also more important is to have the best estimations for the companies that are going to be the biggest emitters, because that's what is more critical. And you can see that on scope 2 there's a bit less concentrated, the emissions, but still, materials, energy utilities have, in terms of scope 2, some of the biggest emissions.

You also have some of the highest R2 in those sectors. In any case, for scope 1, we do get above 80% for the most emissive industries. And for scope 2, we get more than 75% in terms of our square for 22 of 24 industries that we have in here.

Emmanuel
Emilie, I think you can just go to the slides because I don't think you're on the right one.

Raul
Yes, sorry. Yes, so I was talking about this slide. So you can see on the table on the left utilities, Energy, utilities, materials, energy, transportation. Really the red ones, they're the most emissions. Utilities makes 40% of scope 1 emissions, Materials makes 28% and Energy 19%. So you can see a lot of sectors. The emissions are quite small, relatively speaking, but we have quite high R2 for those sectors with the highest emissions. And you can see that in terms of scope 2, I mean, it's a bit less concentrated, but materials is really the highest in terms of emissions, the highest sector with 35%, and then comes energy and then starts going down in terms of emissions. But we have quite high R2 overall. And you can see for insurance, which was indeed one of the industries for which it was really other models really had difficulty, particularly the other machine models from previous literature which actually had negative R2. Because while we're talking about the validation so the test set that is used for validating the final results in. Terms of predictions. And you can see that here we have quite the extra trees achieve 85%.

So I mean the elastic nets less, but quite high R2. Now, what we also have done in the paper, we compared, so it's the next slide we compared with other estimates. So here, I mean the number of companies that we're showing is limited by what is available in terms of estimates from the two data providers we use, the Bloomberg and S&P TruCost. So on the left you have seven, just under 8000 companies, and on the right for scope 2, you have just the same under 8000 companies. And you can see there's differences between what we predict and what they predict. I mean, we've done some analysis. I mean, we do believe our numbers should be more accurate based on the analysis that we've done, but this is something also that has been done after we published the paper. But you can see again, that is in particular also at lowest emissions, that for this particular model the differences could be a bit bigger. But it's interesting that one of the other observations you can see that in particular as you go towards lower emissions, S&P Global TruCost models tend to be more conservative in terms of having lower figures.

Bloomberg on the other hand, tends to be more aggressive in terms of showing higher figures. But then as you go towards higher emissions, Bloomberg, that changes a bit less and becomes a bit more conservative, whereas S&P more or less converges a bit with what we are saying. So it's interesting to see that indeed reflects the fact that the approaches to modeling non-reported emissions are clearly not the and so that's one of the observations that we get in terms of comparing. So, I mean often it's also a related issue. People often say, well, I mean, ESG people don't agree on data. So I mean these carbon emissions, you can see the type of problems there are and people are competing to get the best numbers. And we kind of entered that race in here with our model. And you can see there's disagreement between the data, that is a fact anyway. Now just a word in slide 14 to the next slide on SHAPley values. So by collaborating with essentially what happened since we published the paper, we got to be known within the BNP Paribas group in terms of what we were doing. And we started collaborating with a number of other entities in the BNP Paribas group.

And in fact, the model was adopted a bit by the group and that's why there's a second iteration and the final iteration that went into production. And one of the things that finally came out from this work together with other people in the BNP Paribas Group was the use of SHAPley values essentially to have an idea of what predictors are contributing finally to the prediction. Now, the way that works, I mean, for us, which come from a financial world. We can see it as essentially the same way that when we calculate risk, we can calculate marginal contributions to risk which become additive because they take into account the interactions or the correlations with other variables. We can see SHAPley values a bit like that. I'm not going into how they are calculated. Also it's complicated. You need to use some approximations to calculate you don't really get the absolute exact shut value because that can be quite time consuming. But essentially what you get is something like this for each of the predictors. In fact, well, I chose one of the nicest ones, which is energy consumption. And what this tells you is that if the log of energy consumption is changing, as it show on the horizontal axis, so between minus 2 and 6, well, say between 0 and 4, between 0 and 4, the contribution of the log of the energy conception is essentially linear to the final result you're going to get in particular for scope 1.

And then as you go towards the extremes, that's really where the contribution is no longer necessarily linear. And the way this is done is just by you calculate and you just iterate through your data set all the possible values when you fix the value of energy consumption, that's how you calculate this. So what you have there is a calculation of the model for everything that was possible when in the data set you had the energy consumption at a given level. Now, for the energy consumption, also for scope 2, you can see it's a bit less linear as you increase energy consumption starts contributing less and less. So that's the type of feature do you capture with the nonlinear models, which is essentially what is interesting. And although the results here show that energy consumption, a linear model would not have been that bad, you do capture this bit of nonlinearity and some of the predictors, I mean, the nonlinear aspect can be more important than what I show here. So today for each of the predictors, we can indeed have this type of graphs which allow us to say, well, we have a machine learning model, indeed is extract is complicated.

Some people can say it's a black box, but this allows us to introduce transparency in the black box and say, well, but look, I mean, you have these figures because when you input your energy consumption find it is almost linear relationship except in the extremes where it may not change that much. So that's what we can show and that's quite important and that's why we dropped the less accurate elastic nets in the final model. So the next slide in terms of finally what we have today, without going into the details, because it's more or less based on what I've shown, is just some improvements in automation and just using extra trees. What we have is we actually calculating carbon emissions scope 1 and scope 2 for 32,000 companies for which we didn't have reported numbers today. I mean, we have bigger numbers in terms of what is reported. As you can see here, this is August 2022. We have numbers for which, even if we wanted, the data vendors would not give us what we would need. Now, this universe now is beyond asset management and what we use is really for the BNP Paribas Group.

So involves also knowing about every single company to which we lend, what are the carbon emissions. And anyway, if we were not to do our exercise, we could only really, for quite large number of companies, rely on estimates from Bloomberg, whether we liked it or not. Advantage of having the model is also you can tailor it more to the needs. And sometimes, for example, as we started, we started with elastic necks and extra teeth. Perhaps we want a more accurate model. Perhaps we want a more conservative model. By having our own model, we can also essentially handle all those and really tailor models. We can update it more frequently, less frequently. So that's the advantages of having our own production. So as conclusions, we have constructed. So in the next slide, we have constructed this model. We embarked on this project because we felt the need, we felt pressure and we felt the need. And so based on our contacts with academics, we found the opportunity and we now have this model in production at the level of the group it's at the moment for scope 1 and scope 2. We found that indeed, this model from our point of view, is more accurate than existing models.

The use of machine learning is clearly a big plus. Talk a lot about using machine learning in the financial industry. This is a case where we think it really allows us to produce more accurate data than non machine learning models. And so we believe indeed can be used to predict and report on greenhouse gas emissions with greater accuracy. We mentioned scope 3. So that's something we have been looking, there's been quite a lot of going on there. There's also data vendors that have starting producing. So we working on it, but it's more complicated and we questioning the extent to which we're going to continue working on it. So that's more or less where we are. But it's working process there what I've said, you can find pretty much everything in this paper, which if it's not yet open, it should be open for everyone to download very soon anyway.

Emmanuel
Okay, so thank you very much. Thank you Raul, for the great presentation. So, before I pass the floor to Jeremy, I just wonder, so the result you presented were on 2018 carbon emissions. So now you must have 2020 or 2021. So I was just to know how those results have changed or not. So, a couple of questions from my side. First, in terms of the landscape that means, do you see an improvement and in what size, what scale regarding the reported versus non reported in your universe? First question, and the second question is in term of your relative performance. If you compare to third party providers, is your model still highly competitive? Do you see third-party providers now improving their performance? Maybe that they are using something similar to what you are using? So, just in a nutshell, is there any changes with respect to your original paper and what are the evolutions with respect to the landscape of carbon data?

Raul
Yes, so very good question. So, as I mentioned, I was there at the beginning when this model was put together. Then once this started being taken by the BNP Paribas Group, I was much less involved or hardly involved. So what I know it was done, the model evolved into having all the extra trees. And in terms of what we now use to validate the quality of the model, it's actually the data that is being published by companies that were not reporting before, but for which we had predictions based on the more sophisticated version of the model. And in terms of the results, looking at those companies that have started reporting, because there's been an increase in the number of companies that report these days. And in fact, we found that indeed, we were reporting the numbers quite accurately on average, and we had the most accurate predictions. In particular, when we compared the numbers that other data vendors were forecasting, we didn't look at our scares, we look at are MSCIs and we find that we do have the lowest errors in the model. So we essentially quite happy in terms of the quality of the predictions that we were producing and the models that we have created.

And in the paper, the second paper that was published, those results are published and indeed the one that seems to be closest is MSCI for scope 1, but still they have a higher error. And for scope 2, I mean, we clearly have the lowest errors. So we're quite happy in terms of that. The differences, well, they can be big for some data providers. Thank you.

Emmanuel
So Jeremy, if you want to ask some questions, especially knowing that you're working on how to estimate scope 3 carbon emissions, I'm sure that you have a lot of interesting questions to ask to our comments.

Jeremy Gallean
I hope so. First off, thank you very much for your insight. My first question relies more on what do you do with the information you get rather than how you get it? When reading your paper, you mentioned that having high GHD emissions transcribes a transition risk which can be defined in negative cash flow from fines or operating risk from migrating to a greener competitor. My question is, has your model, which has been rolled out to the entire BNP Group, is it actionable yet in the sense that do you still rely on exclusion lists for your instruments, or has there been a threshold established, meaning that instruments with GHG emissions higher than a certain threshold will be excluded from your action?

Raul
Yeah, well, there's different types of applications. First, we do not tend to work with carbon emissions in absolute terms because that's also not fair. If you run a big business compared to a micro company, our emissions are obviously much larger. So we work with intensities because it's really about the size you have to normalize by the size of a company in some way because some companies are just bigger than other. So you don't want now all of a sudden just say, I'm going to solve the problem by investing in micro caps because they have really low emissions. They may have low emissions, but if they really exposed to electricity produced by coal in terms of intensity, it can be a disaster. So we tend to use data, at least for asset management at a portfolio level, normalized by intensity. In terms of applications, I mean, we do decarbonize our portfolios. So the intensities are indeed used to make sure that our portfolios have lower emissions than the benchmarks market, capitalization-weighted benchmarks. And then there's different targets. We do ourselves have also a net Zero policy. So we want to decarbonize essentially what we manage for our clients.

So we have targets and this is also part of what we use in order to measure and to see if we are on the right path. And there's different approaches and we're also going to change the way we are doing because indeed one of the issues with the carbon emissions and what we estimate in here is that really what we observe typically in the past, once the data is published, is based on reported data that was calculated for previous year. So, I mean, we want to be also more reactive. So we also in our approach, this is part of it, but we want to introduce a more forward-looking component. In fact, we're working on approach which is also going to see whether the companies are actually committed and doing what they say and going on the right path. So are they aligned or are they aligning or they just simply don't care. So we're going to take something awesome of what looking. But this is all part of the toolkit and the data sets that we need to get to that. So that's the type of usage and this is in asset management. Now, I don't really want to talk on behalf of other entities, but they also have similar needs in terms of reporting, understanding of the exposure of the businesses, in terms of reducing carbon emissions on their side, in terms of who they finance and how they finance and detecting, if they are on a good trajectory or not, to decide finally if they're going to continue to finance or not.

So all that is used, and I mean, what we have here is our attempt to just do it the best we can because unfortunately we cannot do this without data. And data, as you could see, is car. So we're trying to really do this in the best way we can. Also by having found out that data from data vendors was not of the quality we thought it could be.

Jeremy Gallean
Well, about that, apparently there is some issues with the data reported by companies less with scope 1 and 2 than scope 3, which has a lot of, since it's voluntary disclosures, there might be some of the 15 categories that are emitted or forgotten about. And about that, I have a quick question about your model, which has extraordinary results and with the increase of mandatory policies and frameworks forcing companies to disclose their emissions, namely the CSRD or the CC 2024 in the US. Do you think that the model can become sufficiently precise every time that it can beat companies which account for carbon emissions using a bottom up approach with lifecycle analysis and EIO, I think is what you mentioned in your paper?

Raul
Yeah, well, it's a good question. We would prefer that we just get reported numbers and that they are of good quality and don't need to do that because then we can do our job, which is manage funds rather than estimate carbon emissions. So, I mean, yeah, fingers crossed that everyone starts reporting and the data is audited and it's of great quality and we don't need this, but in the meantime we need it. So the data is not there yet and anyway, there's errors in data. Maybe at some stage the model will be used. I mean, the model on our side, the way it's used is in particular used to challenge what we get from the data vendors and well, that's already what we find is quite a good use for the model but to really make sure that we understand the data that we have. But if everyone starts reporting even better. Now, what you say on scope 3 is true and scope 3 is not necessarily going to be reported in the same way as scope 1 and scope 2. Indeed, one of the issues also scope 3 is that it can be significantly larger than scope 1 and scope 2, but also can have double counting.

So there's quite a lot of issues with scope 3 but it's clearly something that we going to start using and we're going to start using in this year, later in the year. So there's quite a lot of work on scope 3, we did start by constructing some models but we did not find results as good as for scope 1 and scope 2. And also there's a question how we do it because, well, you have all these components or do you estimate all the components? Which is not really good because in some cases also you don't have data, as you say, when you say scope 3, you don't really know how many of the components have been used, even when it's reported. So, I mean, those are some of the difficulties. So we had come up with some approaches like just reducing the number of components and trying to work with that. But all that kind of is a bit well, I'm not working on that. And from what I know, also there's some new data sets that have been produced by some data vendors which are being analyzed also quite carefully. So there's work going on and there's still a possibility that we have a model which will help us understand scope 3 and report and use scope 3 but it's work in progress and it is indeed much more complicated. And one of the things also that we've been trying is to use supply chain data. But again, supply chain data, the quality is not at the level we really wanted it to be. So it's an avenue that also we started does help a bit, but then, I mean, it's a bit incomplete. So we're also working on supply chain as a way maybe to get and solve the problem. Help solving the problem.

Jeremy
Thank you very much for your answer. I see there are some questions in the QA.

Emmanuel
Maybe you can propose effectively that we so there are two questions. I will pick the first and Jeremy will pick the second, if you're okay, sure. I think it's more just to clarify with respect to your paper and your presentation. So the first question is wonder, as your model is used to forecast unreported data, how can you assess its performance out of sample? So just to explain, in fact, how you compute the out of sample error or performance, if you can just develop on it?

Raul
Yes, the way it was used. And the figures that are reported in the paper are essentially based on typical cross-validation approaches where you just reserve a final bit of data for the evaluation at the very end. That's really how the R2 were calculated. And as I mentioned, what we switched from since the paper, we started doing it differently because we've been doing this for a while and we had indeed predictions which have been put together already over well, the paper just appeared in 2022, but is based on work done 2018 for emissions then. So, I mean, we could use the model to generate predictions for companies that in the meantime started reporting. And so in the second paper, that's really what we're looking at. And we believe that's really a lot more powerful because indeed, we had a model and now we can really look okay, predicted some figures the company published. So how good was our prediction? And that's, as I say, where we see that our model is generating the lowest errors in terms of comparing what was the prediction in the past and what we getting now for that particular date for a company that in the meantime started reporting. So we're putting a lot more focus on looking at those numbers than even at the numbers that we published in the paper that indeed show the quality is good, but it's based on a small sample that we keep 10% of the data set that we keep on the side just after doing all the construction of the model. Really just do the predictions for those 10% of companies and estimate R2. So we moved away from that, and we're looking really at errors based on reported data that was reported exposed.

Emmanuel
Thank you.

Jeremy
I think we missed a question from the gentleman, but it's in two parts. So the second question is, is there some types of assets or sectors which are more challenging to capture from data vendors? For instance, private equity or infrastructure? Also, how do you deal with missing data imputation before modeling?

Raul
Yes. So on the first question, indeed, on the universe that a lot of the companies that were missing, indeed, they corresponded to private assets in terms of missing data in the paper, we kept it relatively simple. Whenever there was a data point missing, look at the sets and the type of data sets. Also in the paper, we have the stats, but essentially we just pushed previous numbers that had already been reported, and that essentially we managed to complete the entire data sets. So in terms of what was missing, it was not really that much. And we managed to complete the data sets just by pushing four numbers that had been published in the past. It's an approach. There's potentially other approaches. When we did the paper, in fact, on the production model, I'm not sure if they did something more sophisticated, but when we constructed the paper, the model that is in the paper I'm talking about, that's essentially what we did.

Emmanuel
Okay, you pick the last one Jeremy?

Jeremy
Ok. Is carbon offsetting complicating the estimates or taking into account to some extent?

Raul
Carbon offsetting, no, as far as I know. I don't see why we to be honest, I'm not sure if we used it, tried it out as a variable I don't know if we have enough data to actually use it as a variable. We didn't I mean, would we get better data by inputting carbon offsetting? To be honest, no. I don't have an answer to that question now. Would it make a difference? Don't really know. I don't see why it would make a big difference. But what we can make is a company that is prepared to pay and buy and offset carbon in one way or the other and continue to emit, still have higher emissions for us, still be reported. I mean, here we're not saying that the company has these emissions and then is buying these carbon offsets. In fact, we don't take carbon offsetting in the carbonization efforts in the funds. So if the company is doing okay, it's excellent. But the number is still the number. So that's it.

Emmanuel
I have a question. Did you look so you already give an answer the private public. But beside that, did you look to the characteristics of the company that report versus the one that do not report? Are they different? Are they similar specific pigeons?

Raul
Well, the one feature that is really just obvious and comes out from the data very easily is that large cap companies publish more. I mean now I don't remember any other features really being common and that large cap companies published is also understandable. It's also general bias on ESG because they have means, they have more means to hire people and to have people focusing on this. As you become smaller and smaller, the costs of having people just calculating these things or doing this get higher. So for as long as you can avoid, if you don't think that that is going to have an immediate benefit on your business, you try to avoid. So that's the one bias and you can see it in the numbers that we showed right at the beginning. When you go to the 3000 larger companies, you already have almost 50% reporting and if you look at the entire universe, you can see that it's most smaller companies that not report.

Emmanuel
Okay, so I think we are close to the end. So just want to thank first Jeremy, thank you very much. Looking forward to your result Jeremy, that you will send to Raul. And obviously I want to thank Raul for sharing his expertise. So great talk. Thank you very much Raul. It's a pleasure. So I just want to take a moment to announce that we will have a next speaker series. This will be this month too. On the 18 of April we will continue our Journey on the Future of Finance and we will be happy to host Alex Edmans from LBS that will talk about ESG and more precisely do company, do corporation need a purpose? So thank you very much. Thank you Raul.

Raul
Welcome. Pleasure. See you. Bye.

Emmanuel
Thanks Raul. Thanks Jeremy.

Jeremy
Thanks.

Make an impact
EDHEC BUSINESS SCHOOL
[Music]

 

[Music]
EDHEC Business School
EDHEC Speaker Series - The future of Finance
Making sense of Green Finance
How a smarter financial sector can contribute to the net zero transition

Jean Boissinot
Deputy Director (Financial stability)
Banque de France

Tuesday, 4 February 2023
6 P.M - 7 P.M Paris time

Emmanuel Jurczenko
Okay, so let's go ahead. Hello everyone, and welcome to the second season of the EDHEC Virtual Speaker Series on the Future of Finance. I'm Emmanuel Jurczenko, director of the Graduate Finance Program. And again, it's my pleasure to welcome you today. So, for this today's session, first I want to introduce our two commentators. So we got David Zerbib, that is professor of Finance at EDHEC, one of our experts in sustainable investing. We have also the pleasure to welcome Alexanne Heurtier. Alexanne is a student in the MSC in Sustainable Finance and Climate Change that is conducted in partnership with Ecole des Mines. And it's my pleasure to introduce our guest speaker for today's session. So we are very fortunate to have Jean Boissinot. So Jean is deputy director at Banque de France. He's the head of the Secretary of the NGFS, so Network for Greening, the Finance System and before joining the Banque de France, Jean head various position at the direction Général du Trésor. He's an alumni of Ecole Polytechnique and the Paris School of Economics. And last but not least, Jean has published recently a book that I recommend titled Finance Verte, published from Dunod. And here is the book.

So, I read yesterday and really learned a lot of stuff. So I really recommend all of you, if you did not already read it, please. And we will have kind of overview of some element that he described in his book. So we are very happy to have him speak today and to help us to better understand what is about green finance, why finance matters for the zero carbon transition and does climate change matter for finance. So, without further ado, I'm turning to Jean. Welcome Jean, and the virtual floor is yours.

Jean Boissinot
Thank you so much, Emmanuel, for the very kind words. So let me pull up my slides. All right. Is it working? Perfect. Thank you very much. So, again, thank you for the invitation to speak about green finance. Indeed, my keynote of today is, I would say, loosely inspired by the book. There's a few more things in the book, but I wanted to have a discussion with you today about making sense of green finance because it is something that seems of use the idea of green finance that everybody is speaking about. But in fact, there is a lot of confusion, I think, around it. And just to illustrate the confusion or the diversity of the perspective that you can have about green finance, I played with a few logos of things that you can loosely relate to green finance. And I think you would have given a slide twice as giga. I could have filled up with some more things. So it's a bit of a disorganized field, I would say. And part of the specifics of green finance and the problem of green finance, I would argue, is that everyone is calling green finance something that he sees or she sees as green finance.

And there is very little consensus around the idea, which in the end means that some would argue that green finance is the solution and some would disagree and very much argue that it's pure illusion. And I think that this keeps the discussion in a place where we cannot see exactly what finance has to do with the transition. And I would blame part of this confusion, I think on the advocates for green finance because you can find good, bad and very ugly reasons to promote green finance. The good reason to me, and I will hope that you would be convinced at the end of the keynote that this is how we should be thinking about green finance. The good reason is that we need to finance the transition and to deliver a climate consistent capital allocation. The bad reason perhaps is an argument that relates to the fact that public finances have been stretched to the limits over the past ten to 15 years and especially in the past five years. So you find people arguing that because of this limit that we have reached with regard to public finance, maybe there is a role for private finance to pick up the slack.

And to me, what's a bit wrong with this idea is that in fact, as we are looking at a complete transformation of the economy and the financing of that transformation, maybe we can look at precedent. And in no places or in no previous past episodes, I could see public finance being the main driver of an industrial revolution. Maybe the ugly reason to me of promoting green finance is the idea that financial policies will make it up for the lack of ambition of climate policies. It is hard to put a price on carbon, it is hard to have proper climate policies and maybe we can let the financial sector do the heavy lifting. It has to do with the idea of advancing a value based agenda also. And I think that's something that is raising a lot of questions and raising pushback from many fronts. And overall, when we argue for green finance on the basis of not very good arguments, we run the risk of over promising basically what finance can do and under delivering in the end what finance is able to do. So before I jump to trying to substantiate...

Emmanuel
Jean, just a question is it possible just to increase the sound?

Jean
I'll try to keep the mic further. Sorry, apologies for that. So I'll speak less with my hands, which you didn't see. Sorry. So before jumping to the core of my demonstration, just a bit of an historical detour into how I get involved into green finance and I think what really happened around the COP 21 in Paris, which I think was fundamental for this development. So in 2011, 2012, we had a big discussion in France around the lack of progress in addressing climate change. It was just before the Grenelle conferences and the Grenelle laws and so on. And in fact, we see little progress in reality and finance was identified as the possible culprit for all that lack of progress. And the rational behind that was twofold, was mainly the fact that finance was seen as being in bed with the no transition team, the all majors and the like. And that's true that the financial system, the financial sector has a lot of connection with these type of companies because they've got a lot of friends and this is clearly a type of client that would deserve special attention by the financial actors. But I think that beyond that which we should not overestimate, there was also the idea that finance goes with the flow and we are only financing what the economy is doing.

So if the economy is not transitioning, well, it's a bit difficult for finance to transition ahead of the economy. And I think that's a more reasonable argument. But the real question behind that is not whether finance is responsible, but is or whether it's a good scapegoat for the lack of progress. But is it really the case that finance is holding back the transition? I mean, we all know that for the transition to happen we need typically climate policies being implemented and investment decisions by firms and by households and so on. So is really finance holding back the transition? And we came out of that debate in my team with a better understanding of maybe we have to ask ourselves is there something there that we should be looking at more closely beyond the kind of easy argument to put all the blame on Finance. And soon after that, in fact, we were asked to work on the COP 21. And the one key steer that we got from the Finance Minister for whom I was working at the time was please don't let finance get in the way of an agreement in Paris. And for that to happen, we had to go past the idea that finance matter for the transition which is obvious, you don't have a transition without investment and you don't have investment without finance. And that's true both in general in the economy, but also in the context of the COPs because of the commitment that advanced economies have taken toward less advanced economies. The 100 billion question typically. So there is no matter disputing that finance matter for the transition. But the real question is the other way around basically. What if climate change matter for Finance? Because if climate change matters for finance, then it's a whole new set of things that things that we can play with basically too to make some progress. And in answering that questions, basically you can identify three main drivers. The first one is related to the no transition team arguments and it's about financial institution protecting their reputation and it's a communication battle. I'm doing some nice things, but you're not doing anything else than wind-washing, et cetera, et cetera. And it can eat up quite a lot it makes for good headlines but very little progress on the ground. But that's one driver to get the attention of financial institution on the transition.

The second driver is more related to client demands and preference. And that's the idea that and some financial institution would argue about that at that time and even today, if my clients want green products, green saving products to finance green investment, I'm very happy to follow them. And if they want something that is less green, then so be it. And that's in relation, I think, to the idea that finance goes with the flow. The problem with that is that because we are not speaking of a set of preferences that are completely independent from one another and disconnected from reality. What you are doing when you are engaging into green finance only on the basis of your clients demand? You're basically building up a balance sheet that Alpha Fit will thrive in the transition and the other one will fail. And if there is a lack of transition, Alpha Fit will fail and the other one will strive. And I think it's not a very good management, good risk and business management to basically build up your activities and your company in such a way that in any state of the world you're losing half of what you have done. So I think the third and main driver for looking at what climate change means for finance is really about focusing on the economics of climate change and the net zero transition.

And that's what I would try to get around with you in the coming slides, having in mind that I will be a bit quicker than with an uneducated audience. So this is, I think, very common ground for all of you, the fact that we are witnessing a rapidly rising global temperature and that these anomalies we cannot explain them without factoring in human causes in the models that we have. So this has something to do with the GHG emission that we are producing. What is very important is also to keep in mind that we are witnessing the very first impact of climate change. But it is going to be a lot worse if we let it go and we don't address it. And in that respect, 1.5 degrees is not so good, but not as bad as two degrees and certainly not as worse, so to speak, as an increase of four degrees. So there is really something to be done to minimize the level of climate change we are confronted with. And because that level is entirely dependent on the concentration of CO2 emissions, that really means that we need to get to a net zero situation.
And that brings me to my next slide about carbon budget, which is what people don't completely realize. Net zero is not an option. The only question that we have to ask ourselves is when do we reach net zero because we have to get to net zero if we want to stabilize the climate, whether it is to 1.5, 1.7, 2 degrees or even four or five. And that gives us basically the time frame for the transition. Having in mind also that nothing in that is completely there well. Nothing is deterministic at all. It's really a shift in distributions of distribution. So we are basically making a bet on the future and trying to find the safest way to remain in a place that is comfortable enough. So some would argue that we are clearly not where we should be, and I would not disagree. But I would also like to highlight the fact that we are making some progress and that things are maybe starting to be a bit less underwhelming that they used to be. Not completely overwhelming, but less underwhelming, I would argue. So basically we have made a lot of progress if we compare to where we were before the COP 21 and if you look into and what matters is the graph at the bottom right of the slides.

Basically where we stand right now, we have with the current policies, a 7% chance to remain below two degrees. And that's not a very comfortable place, but if we manage to implement the NDCs, these seven goes to 16. And if we follow through with all the net-zero targets that have been adopted by countries over the past five years since Paris, basically, we've got more than 80% chance of staying below two degrees, and we've got a fairly good chance of staying below of 20% chance of staying below 1.5 degree, which is really what we should be aiming at. So clearly we are not where we want to be, but we are really making a lot of progress if we compare to where we were less than ten years ago. And part of this progress is, I would say, predicated on the fact that the economics of the transition have been changing a lot. We are witnessing an industrial revolution that is happening and it is happening not because of a lot of innovation being rolled out in the economy, but because a lot of things are happening due to a constraint basically. And this is somehow an industrial revolution in reverse motion or with a different driver.

But what is quite clear is that now we have the capacity to produce electricity from renewable at cost and soon at scale. And that's covering about 40% of all the emissions. And that has a lot to do with the increasing the scale of what we have been doing with regard to installed capacity and what we have learned along the way. And when you look at the latest IPCC report, basically you see that we have technical solutions for almost all problems related to GHG emission and we know what to do. I mean, maybe it's hard to implement these policies, but we know what to do. Moving from the microeconomics to the macroeconomics of the net zero transition. It's also very important to understand that beyond making sure that all these investments happen what really matter is not just the investment but also what we will do, but also what we will not do. And if you look at it from a macro perspective basically the additional investment that we need is in the vicinity of a few hundreds of billions of dollars every year. So something that the financial sector knows how to deliver. And basically if you look at the transition from this macro perspective the transition is a capital allocation question.

It's not about finding a lot of new resources and investing in difficult stuff. It's really about making sure that the capital allocation is delivered as it should. So what about green finance in that context? In that change of perspective basically I would argue that we should frame climate change as a macro development. That means two things basically bring nature back into the picture make sure that we really account for the material impact of climate change because it will have very real macro consequences and understand that there is no alternative to net zero. And the second aspect to it is really to understand the scale and the dynamics of the industrial revolution that we are facing. And again, if you combine the question of no alternative to net zero on the very limited time frame that we have and the fact that it's technically feasible and economically reasonable to transition our economies, basically we are in a place where it will be a macro question, not a micro question. So it's not something that will be a gradual adjustment. It's something that will, I hope, most probably happen in a limited time frame with big consequences on the macro side.

And that's the reason why a climate change is meaningful for finance. It's meaningful for finance because we need to grasp the full scale of the climate opportunity but also because finance is in the business of building bridges to help the economy develop. And it's bridges that goes from now into the future and we need to reassess basically the way we were thinking about this future and really factor in climate change and the transition into all our financial decisions. Which brings me to what I would say is a better way to look at green finance by distinguishing and to bring some order to the very diverse picture of the beginning is to distinguish between the visible phase or the visible side of green finance, financing, green investment. And it's a question of, well understanding the risk return of green projects. It's about managing technical risk, it's about technological risk, it's about taking into account the longer term perspective of these investments. It's about taking into account the system dimension of this investment and the fact that there is a lot of system externalities into these decisions. It's about recognizing that we are facing takeoff risk that we are dependent on policies when we are taking this investment decision and financing this investment and it has brought a lot of financial innovation think about green bonds, think about a few others, blended finance and whatever and it's about finding the right way to pair up the policy support and the financing basically. It's also honestly a question of how to make sure that we are providing as much green asset as the investor side wants. Because we are in a position of not having enough projects and assets to feed in the demand side of the market and if we are not able to do so, then we will have a greenwashing problem. And greenwashing is just basically the volume question, the volume version of a price bubble. It's an unsociable demand chasing after too few assets. But beyond this very visible face of green finance there is also I would say the hidden face of green finance which is really where finance matters. It's not about financing wind farms and PVs. I mean that's important but that's not the main contribution of finance to the transition. Our main contribution to the transition is to make sure that the capital allocation we deliver or the financial system is delivering is consistent with the climate change to keep. And this is something that really goes, I mean the first side of green finance is maybe a few hundreds of billions or a few thousands of billions of dollars every year. The dark side is really about every single investment decision to be reassessed against the perspective of the change of climate on one end and the transition on the other end. And basically you can rearrange everything that you can. Think of in the field of green finance under these two items and you can find that it is something that would speak to every bit of the financial system. So I think it's a better framework basically to think about what we are doing as financier in that respect rather than arguing that it's about thematic funds, it's about ESG investing or it's about green finance and blended finance et cetera, et cetera. I think it's good that we have a collective understanding of how each bits and pieces is fitting with one another and how this is contributing to the bigger transition. And maybe to conclude, just let me share two big questions that I think are important at this stage.

The first one is related to the way climate risks are priced in or not. In fact, I would argue that most of the policy decision that we are taking now whether it is a microprudential regulation, macro prudential policy, monetary policy operation and so on in fact we are making an implicit assumption around the fact that the climate risk are priced or not in the market. And whether it is the case or not we should be considering very different options. So it's a very important questionsn on the policy side I would say. On the private side, it's also quite clear that a lot of private sector practices are in fact also related to that assumption. And if we look carefully, and I wish there were more studies on that and more research on that, what we can see is that climate change is indeed increasingly priced in, especially over the past five to seven years. But it's unclear to which extent these developments are priced in. It's not quite clear that they are all priced in out of the right baseline or that we could basically infer some kind of market implied baseline with regard to the transition. So one big question is really to try to understand what the market is telling us and is pricing and not pricing because that's where a lot of good or bad decision or the basis on which a lot of good or bad decision will be made.

And finally there is one question that keeps us, that is haunting us basically once we have started to work on climate is there something else beyond climate? And I will argue that on the basis of the work that we have done in the NGFS, we are actually currently doing a lot of reading and research. I would argue that indeed it's not just climate change that matters. There is a lot of the more general problem is nature blind economic decision. It's the fact that we are completely forgetting or taking for granted a number of things that we use and that we should maybe question the continued availability or the maintenance of the quality of them in the future. And that's clearly something that we need to reassess basically. One line of arguments is to well question whether this is somehow all be summed up into climate and purely climate change is something that has a number of indirect impact on, but it's not just climate. And for example, if you think about freshwater, clearly climate change will have an impact on freshwater availability but a lot of the main problems are not related to climate.

They are related to the way we manage the current resources that we have. Something that is a bit tricky is that the risks that we are starting to look at, they're in fact a lot more local and granular than climate change. Climate change is in fact a global problem and something that we can rather easily understand. But what we are looking at is can be global in a number of cases but is also very often a lot more local and a lot more granular. And when I'm saying that, it does not mean that it is less material from a macro perspective, just that it is a lot more complex to understand and identify. And this complexity is in fact the problem that we are facing and we collectively need I think attractable framework to account for this more local and more granular phenomenon. I think and I would argue that the current way of thinking about physical and transition risk is the right way and can be generalized to other nature-related developments. Now, the question is everything that was easy to grasp and had to do with the ton of greenhouse gases emitted into the atmosphere, basically that summed up the problem, or what's the equivalent of that, or how to think about nature in general.

And what I'm showing you on the right-hand side of the slide is a personal, at this age, a personal thought around how to generalize the progress we have made with regard to climate change. Basically connecting the planetary boundaries, which are the things that we will have to collectively address and that would matter for our societies. To ecosystem services, which is the thing that we use without thinking about it and our economy is dependent on. And if you think about it and try to generalize what we have done on climate change, basically our dependencies on ecosystem services that are unpriced and overlooked so far can be related to physical risks. On one end and the impact of our economic activities onto the planetary boundaries basically has to do with the transition risk. I mean, that's because we will try to manage the system to bring them back within a manageable within manageable limits that we will raise a number of transition risk. And then the question if we want to generalize a bit or to use that framework for expanding what we have done on climate to other nature related risk. The two questions that we need to address is to know whether going from the planetary bondaries to the ecosystem services that matters is enough or if there are other development that we should take into account and also to understand which ecosystem services we are using that we are critically dependent on.

And I would argue that from a macro perspective, they are not all born equal, I would say. But the question to have this track table framework, the question is which are the ones that we should for sure include into our city? And that is about it. So, very happy to get your reactions, comments, questions and remarks.

Emmanuel
Thank you very much, Jean. So right on time. So, thanks. So now I let David and Alexanne ask some questions or reactions.

David Zerbib
Yeah, well, thank you so much, Jean, for this highly interesting and insightful presentation. You've clearly explained what green finance is today, what we should expect from green finance and what we should not expect today. I'd like to ask you a kind of slightly provocative question to go straight to the point. So we are in a world where the carbon tax is probably not ambitious enough, especially in some areas in the world. We have evidence that the preferences for investors to purchase green assets is not that huge. And at the same time, we know that we want to re embed finance within a nature ecosystem. If I may say within nature or constraints. So my question is to which extent green finance can be coercive? What in your opinion is acceptable and what is the limit of acceptability? Can we constrain investors not to invest in some asset or compel them to invest at least x percent of the balance sheet into green assets? Or from a central bank point of view, there are different avenues in terms of yeah. So I leave the floor open to you for the answer.

Jean
Thanks a lot, David. So perhaps it's the time that I should stress a disclaimer that was on the first slide. Everything that I'm speaking about or any views that I'm expressing tonight are clearly personal ones. It's a personal answer that I would give to your question. I fully agree that we don't have the climate policies and the environmental policies that we need at this stage. And that's a problem. That's very much a problem because in fact if we want to manage the transition collectively we need all actors to play their part. We need the policymakers to set up and implement the right policies. Right policies means a lot of things. It means ambitious enough, it means sustainable policies, policies that can be sustained over time. It means policies that are politically acceptable. So it means also just policies and so on. So I think it's important that we have these policies put in place. It's also important because without these policies basically none of the decision that are taken by the economic actors will be fully aligned with the objective of re-embedding economic activity into nature. So we need these policies, then we need the investment of firms and households to adjust to this new set of policies and then we need the financial sector to understand the implication of the policies so that basically it is able to discriminate between the good project and the bad project early on, but also to be there to accompany the transition.

In fact, if I wanted to summarize my view on the transition is that it's a tango that needs three bits to be danced, basically. So going to your question about coercivity basically no. Before going to that coercivity question your comment on the preference for green assets that you assess not to be very material or not to be important enough. I think there is a lot of appetite for green assets and the problem is perhaps more, I would argue more on the supply side of producing these green assets than on the demand side. But the problem is that what finance is able to do is always limited by the risk-return of the project basically. You cannot ask a normal financial institution to finance a project that is not bankable especially if you're looking at financial intermediaries who have to respond to the two sides of their balance sheets basically. It's impossible for them to be making that kind of arbitrage of, you know, giving precedence to the asset side. Or to the liability side. So they need to find the right place. And that's what the policies are there for, basically reconciling the two parts of the equation and making sure that the project that should be bankable are bankable and that the project that should not be bankable are not bankable.

So that's where I see the issue. And then to your coercivity question, to me it's really a question of what we think is the social value of the financial sector. Is it to bring fund to one place to another or is it to allocate capital? If we think that the main value of the financial sector is just mechanically to move some resources from one end to the other in a very automatic way, I would say, maybe we can go for some coercivity. But I mean, that would require a lot of planning, a lot of information that I'm not sure anyone have in the system. If we think that the social value of the financial system is really to deliver capital allocation that is informed by a lot of individual decisions, a lot of tattooment, a lot of bets and so on. But that in the end goes into the right direction, then I would fear to interfere too much with that process by being coercive and by saying, okay, now what you need to finance is x percent of your balance sheet should be devoted to Green. It's a question that I'm faced with regularly by investors saying if someone can tell me what is green, I'm ready to finance.

And my answer to that is well, no one will tell you what is meaningful to finance. What really matters is for you to play your role as an investor and make sure that you are putting your money where it should be put in light of basically the future as you sit and as an informed view of the future as you can.

David
Thank you very much Jean

Emmanuel
Perhaps just to jump to that the challenge with respect to this capital allocation is obviously how you assess the performance and the risk, right? And we are more in an uncertainty setting than in the risk setting. So I just wonder what we need to do in terms of progress toward the direction because you need to have some information, you need to have some tools in term of asset allocation. So what is your vision about where we stand today and what we need to progress toward this very important mission, which is the capital allocation to have the right one with respect to the climate risk?

Jean
In a nutshell, I think we have started to move in the right direction and we are clearly not where we should be yet and we should not have a sequence approach to the problem because we don't have the time to have this sequence approach to the problem. I mean, develop the perfect data and develop the perfect models and so on and when we have perfect everything, then act upon those. We are born to start experimenting and to innovate along the way. But I'm really mindful about the urgency of what needs to happen in the grand scheme of things. I'm also quite impressed by the strength of the momentum in the financial sector when things are done in the right way. Typically in the NGFS we are a network of central bank supervisors. We have joined forces to develop the tools that we needed to do what we think we have to do with regard to climate change. So improve supervision, make sure that we are able to monitor climate-related risks at the macro level, take into account climate change in monetary policy and so on and so forth. So it's really central banks trying to develop the tools that they need because they have, as everyone realized, that they know way too little about what to do, but we need to go fast.

I'm really impressed by the fact that five years ago it was some kind of visionary but completely out of the monet by eight institutions who said together well, we need to collaborate to find the solutions. And we are now 140 institutions. We are collectively supervising all of the global systemically important banks, for example. So this is a group that is not marginal by any way you look at it, from advanced economies, from emerging markets, from developing economies and the work is progressing steadily and really faster than everyone expected. I mean, five years ago you would have told me that in five years we would have climate stress testing for real. I would have be very happy. But I would have perhaps be a bit cautious about our collective ability to deliver. And that's what has happened. Also in the financial system this is happening. You may remember that a bit more than two years ago the Single Supervisory Mechanism, the supervisory arm of the ECB, has set up supervisory expectation with regard to banks for what they have to do or for what they should be able to do on climate.

And they took an initial picture of the practices in the banking sector and basically we had 25% of the banks at the time had at least a basic understanding of the risk. So clearly we were starting from nowhere. Two years later they took the same picture and this 25 is now 85. So a lot of progress in the past two years. What the ECB has also assessed is that basically all of the banks have material gaps. So not everything is perfect a long way to that situation. But still they have been able to identify best practices for almost all of their supervisory expectations. So it's possible to go where the supervisors think you should be operating to be able to deliver this climate consistent capital allocation. Which means, basically having the right data. Even if it's not perfect, you start measuring them. Having the right credit decision process, having the right sectorial policies, having the right management of your continuous management of your risk and so on and so forth. So it's a work in progress that we have to accelerate. But clearly, when we decide to go into that direction, it's something that we can deliver.

Emmanuel
Thank you, Jean. So, Alexanne.

Alexanne Heurtier
Thank you again for your presentation and for your book. Very insightful. I may have a more macroeconomic question for you regarding a subject you mentioned. So you're talking about the heterogeneity of the economies, but also of responsibilities. You also talked about the commitment of $1 billion for the developing economies, but the targets is still not met. What do you think about the loss and damage fund that was announced at the COP 27? And what maybe a second question. What could be the role of private institutions to maybe be complementary to the public targets they have?

Jean
So maybe two ways of looking at your question. I think it's important that we collectively keep in mind this common but differentiated responsibility that we collectively have for climate change. And also keep in mind that it's a bit more complex than just advanced and emerging and developing economies as of now. So we need to also update the way we were thinking about this $100 billion pledge. But still, it's indeed a very important question because the transition is not something that will happen if advanced economies only look after what has to happen in advanced economies and so on and so forth. I mean, the transition is more than a collection of domestic transition. There is a global dimension to that, and there is a lot of capital flows to be scaled up to finance investments in places that lack the resources to fund this investment. So it remains a very important question how to scale up these capital flows. And to be completely honest, that's a question we are currently in the NGFS, but a few others among the multilateral development banks and others are looking at how to make it real that these capital flows happen, not because we have pledged they should happen, but because it's a necessity that they happen for the transition to happen.

And it's both feasible and manageable. So on the loss and damages fund that was set up in Charm el-Cheikh, I think it was a longer virtue I mean, that's again, a personal comment. It was a longer virtue decision. But I fear as someone with a finance mind, that we are missing half of the decision basically. It's as if we have provided an insurance against the worst of climate change, but we have not kept the risk that climate change will keep on worsening and worsening and worsening. So I think it's something that is begging for a strengthening of the commitment of everyone to transition away from fossil fuels, basically. And if we can have these two bits, then I think we can start to think a lot more realistically about what needs to happen and what can happen with regard to cushioning the poorest economies and the less developed economies from this risk. And I mean, the case in mind is what has happened in Pakistan over the last year with a dramatic heat wave in spring and early summer moving into a catastrophic "mousson" in September where literally 12% of the Pakistani's land that was underwater.

The shock to the Pakistani's economy is 10% of Pakistani's GDP. So it's something that is really I mean, we need to find ways to make sure that we know how to usher and rebuild, build back after these type of shocks. But it's meaningless to try to build back after these type of shocks. If we don't do two things first, limit the risk, which means that transition accelerate the transition and second, build up resilience and adaptation when we rebuild after the shock. And in fact, we don't need to wait for the shock to embed adaptation. Which I think brings me to trying to answer to your last bit, which is what's role of the private sector for loss and energies? And I would here to say that to me loss and damages is not a private finance question, it's a public finance question because I don't see the business model on which you can build up inflows and outflows early mobilization of resources and then payment back to the investor. In that context, maybe I'm wrong, but I think it's something that is a bit tricky but something that I think is very important is embedding adaptation into the mindset of financial institution and especially enhance the way insurance is contributing to solving the problem.

Basically, if we could reveal the value of ex-on-play adaptation to limit the cost and well, the risk and the cost of climate change, I think there we could find some kind of business model which the private sector could play a role in but when it is to only indemnify for the losses and the loss and damages I fear that it's really difficult for anyone who is not ready to lose the capital that he or she has invested to be killed in that field.

Alexanne
Thank you very much. Maybe Mr. Zerbib, you have another question?

David
I was wondering whether we could leave the floor to the audience because we have only six minutes remaining.

Emmanuel
Yeah so, might be able to pick several questions. So I will take one coming from Shaia. Do you anticipate debt and equity capital to play fundamentally different roles in green finance?

Jean
I would argue that in fact, so typically green bonds to me, if you understand it well, is a great instrument because it's an instrument that is bringing some practices of the equity market into the bond market. Basically, to me it's an engagement device for debt investors. So I think obviously we will not mix or we should not be confused about the role of debt and equity on both sides. But I think the two of them have something to play and the two of them may be learning from the book of the other in that respect.

Emmanuel
Thank you. Another question which is related to, let's say mine. Considering the lack of historical data and the assumption that markets currently don't correctly price climate risk, how can models that seek to price climate risk be validated? Basically how we can be confident that these models are working. So, question of model risk?

Jean
Yeah, that's a very good one. So I think that first it's good to make sure that when we say model, we can mean very different things. And when you are looking at economic models, you can play with a lot of exogenous that can tell you stories about the impact of climate change. So in fact, what you need is to bring together the type of models that you are using more in the central world with the type of models that comes more from the academic side of the conversation. And in fact, that's part of what we are doing in the NGFS, in building the NGFS climate scenarios. But yes, it's important to have in mind that some models can speak about something that we have not seen in the past because they are built in such a way that you can play with the main drivers that you see for the future. And we need to have a better dialogue between these models, I think, and the financial models which very often are built on the idea that well, the past is some form of equilibrium that will keep on repeating itself, which we know is completely untrue.

Maybe back to the question I think we also need t,o no, I won't be answering right now. I think something else we need to do is also stop thinking in terms of in a too probabilistic way or trying to make prediction. We need to basically stop optimizing and thinking more in terms of obviousness, basically. So maybe we have models that are not very good, but because we know that they are not very good, we should think about using them not to optimize or to maximize something, to maximize a return, but very much with regard to minimizing the risk that we are making the wrong decision. So we need to perhaps change a bit the way we think about models and the way we use them. That being said, I think it's not completely well, it's not too much to ask to basically tweak a discounted cash flow model, for example, to start, see whether the model that we see, the price that we see are in line with what we know about the rise or the future damages of climate change, for example, or the rise in carbon prices and carbon prices and so on.

So it's not something that we can say, well, we know for sure that it's price or not price. It's more like to what extent can infer something about these risks that we know for sure to be rising are or not reflective in the mean? I hope it was clear enough. I'm sure it wasn't, but I hope it was.

Emmanuel
Okay, thank you. Perhaps last question for today from Laurent. What are your views on the fact that the political momentum and rules seem to diverge between Europe, US and China? Kind of geopolitical question?

Jean
Yeah, I think it's a good question. I hope that in fact, the economics of the transition are starting to be too powerful to be ignored. And I think we can have reached that stage so that cooperation between the big blocks can in fact be reestablished because we have a common economic interest into moving toward the transition and we have also, to be completely honest, a common interest in avoiding the worst of climate change. So the current geopolitical situation is not very conducive of an accelerated transition, but it might still be the case that we can manage it and we can manage the adverse consequences of the Splack of cooperation between the block.

Emmanuel
Okay, thank you very much. I don't know if David or Alexanne want to add something before we close this session.

David
Just to say thank you again for this lighting presentation.

Emmanuel
Okay, so thanks to all. Thank you again, Jean. Very great presentation. For the one that wants to go a bit further, read the book before we close down, just take a moment to announce that there will be a next speaker series that will be on the Tuesday 20 March, will be a bit related. We'll have Alex Edmans from LBS that will talk about do corporation get a purpose. So, thanks. And Jean, if you want to conclude.

Jean
Yes, maybe a final word. What I am very convinced of is that in fact, every bit of the financial sector has to play its part to the transition. And the confusion that I was speaking of at the very beginning is in fact something that we should be leveraging off because we need people in banks, we need people in big asset managers, in private equity firms, in institutional investors, pension funds, insurance companies and the like. We need all of these players to in fact contribute to the transition. So there is not one single solution. But what we really all need to see happening is this kind of change from the very bottom to start to realign what we are doing with our common future.

Emmanuel
Okay, thank you very much, Jean. And from our side, we are preparing the future generation of finance. So happy to contribute at our level. Thank you very much.

David
Thanks a lot.

Emmanuel
Thanks again, Jean, for taking the time and looking for one.

Make an impact
EDHEC BUSINESS SCHOOL
[Music]

 

https://youtu.be/VvJJG7_fuaE

[Music]
EDHEC Business School
EDHEC Speaker Series - The future of Finance
ESG Ratings

Florian Berg
Research associate,
MIT Sloan School of Management

Tuesday, 13 December 2022
6 P.M - 7 P.M Paris time

Emmanuel Jurczenko
So hello everyone. Welcome for last session of the speaker series for this year. So, I'm Emmanuel Jurczenko. I'm the Director of the Graduate Finance program here at EDHEC. It's my pleasure to welcome you today. So I first want to introduce our two co-moderators. So I'm happy to have with us. Abraham Lioui. So Abraham is a professor of Finance at EDHEC. He's one of our experts in ESG investing, especially when you get some uncertainty around ESG ratings. And we got also Marianne. So Marianne Tocan, that is an MSc student in our MSc in Sustainable Finance and Climate Change. That is a joint program with Ecole des Mines. Okay, so it's my pleasure now to introduce our guest speaker for today's session, Florian Berg. So Florian is a research scientist at MIT Sloan School, Florian have written a bunch of papers which are very topical, and it's great. I asked Florian to do a kind of medley of his four papers on easy ratings. So there is one paper that just have been published recently in Review of Finance, which is Aggregate Confusion the Divergence of ESG Ratings, which is journal paper with Julian Kölbel from University of Zurich and Roberto Rigobon from MIT, but also other papers, very interesting, on the impact of noise measurements with ESG ratings.

We will talk a lot about instrumental variables and another paper on the Economic Impact of ESG ratings. So we are very fortunate to have Florian today with us. Without further ado, let me turn him to Florian. Florian, welcome, and the virtual floor is yours.

Florian Berg
Thank you so much, Emmanuel. Thank you for the kind introduction. Let me share my slides. Yeah.

So today I'm going to talk to you about ESG Measurement and the Impact of ESG ratings on firm behavior. Just to start, as Emmanuel said, I'm a research scientist at MIT, and I started there five years and a half ago. And at the time, there was a lot of research that had been written on how ESG ratings do they cause stock returns and those kind of questions, really, this financial impact of ESG ratings. But very little research had been done on the ESG ratings themselves to explain actually what is in there and also why they differ between providers. And so with Julian and Roberto at that time, we start downloading those different data sets from different raters, and we saw that actually, they're kind of constructed in a similar way all of those ratings. And there was a lot of aggregation because those ESG ratings, they normally have from 38 to 282 different indicators, such as discrimination, water stress, climate change risks. And so for us, there was a lot of confusion and a lot of regulation, and hence the title of the paper. And also the project that we started with that paper was born Aggregate Confusion: the Divergence of ESG Ratings.

Here is the mess. As you can see on the left hand side. So basically, this is a sample is out of six raters. The six biggest S&P Global, Moody's, MSCI, Refinitiv and so on and we have a common sample of 924 firms. And here we just plot the firms where the ESG ratus agree the most and where they disagree the most. And so what you can see actually on the left there are fairly a lot of companies where they very much agree and those are very big companies such as Apple for example. The ratings are fairly similar between those raters. So you also have Morgan Stanley as a big bank or Heineken as a big beverage company.

On the right hand side, this is more messy. You see that there's actually quite a lot of disagreement between those ESG raters. And so we rescaled all those ESG ratings to make them comparable, we normalized them. And so this is actually the X scale is expressed in standard deviations. But intuitively what it means, if you have go from zero to minus four those are four standard deviations. That's a massive change that's really very big. And here you see Barrick Gold Corporation,  AT & T Inc.. So there's also technological companies. There's Johnson Johnson, and there's also the Royal Bank of Scotland. So also a bank, bank of America for example, like on the left hand side. So we tried to explain why actually some companies diverge or why they disagree for some companies more than for others and we couldn't really find a driver. It wasn't because they were part of a certain industry where it was hard to assess the sustainability practices of that industry and it was also not related to size in our sample. And so we actually then thought about explaining this disagreement. And for us there are three reasons why those ESG ratings disagree. And here is let me explain those ESG ratings first and why they disagree. So on the left hand side, R1, the blue big circle is ESG rating by one rater and R2 is by another rater. Those are the final ratings. And as I said, there are several indicators that feed into this overall ESG environmental, social and governance assessment.

So you have the ESG dimension where you typically have physical climate change risk or CO2 emissions or water stress and anything related to pollution. You have BS where you have child labor discrimination, human resource development, and then you have the governance dimension where you have corporate lobbying practices, word independence and those governance indicators. And so what we see here, what we actually in our paper did, we have an assumption, and this assumption is that fundamentally the raters actually want to measure the same thing. For example, discrimination. If I want to measure discrimination then I really truly want to find out how historically disadvantaged groups within a company are treated. But I can't see really observe that. So what I observe is for example, the difference in salaries or differences in how often someone gets a promotion over another. But those indicators, as you can see, they're not perfect. So these ESG raters, they take those indicators and then they put them in context and try to figure out what actually the intention of the company is and come up with an indicator level score on discrimination.

And this is for example just the lowest A1. So A1 here is the attribute that would be discrimination and A1 is the indicator. So the outcome of rater one and since they probably also use different data sources because some actually send out questionnaires, some look a lot at controversies and again the choice of the proxies you want for your measurement and then your model, your measurement methodology will differ. So those raters will come to different conclusions. So rater one will have a different score than rater two.

Of course they're most likely very correlated but they will not say the same. It's the same than Emmanuel and myself. We will rate a company about discrimination and since we can only see it from the outside, but even probably from the inside, we would come to different conclusions but probably very correlated. So, and then you have also as you see here, so there are different attributes that each rater measures and some raters only measure an attribute another doesn't. For example, the green circle, I1,3. So rater one measures the third attribute but rater two doesn't. This might have actually cultural reasons because for example, me, as a German. For me, obviously I don't like nuclear energy and as a French person you would say wait, this is the greenest energy we have at the moment. And so for me, when I moved to France actually in 2003 after my high school. Growing up in Germany, there is a very much a big anti-nuclear movement, of course. And then coming to France, I get my EDF bill and it says we probably have 100% green energy because everything is nuclear. For me that was a little bit I disagree of course, having lived in France for more than twelve years now I'm pro nuclear, but that's a different story. So you see that those cultural differences will also be reflected in those ratings. So some raters will assess an indicator and some others don't, or an attribute. And this is what we're going to call scope divergence. Then you see when now the raters know what indicators they measure, what attribute they measure and how they measure it. Now they need to aggregate it to a rating and for that they need to come up with a weight because you can see that physical climate change risks might not be as important for you as discrimination or the opposite.

You think that discrimination is more important and this also often depends on different industries because for an aluminum factory CO2 mission is much more important than for an office space provider.

And so this is what we call weight divergence. So here we have measurement divergence, scope divergence, what do I actually look at, what do I measure? And then weight divergence, however

Okay, so this is actually what an ESG rating is. Let's look at now, since we know what ESG ratings are, let's look at the high level correlations.

High level correlations between ESG raters. And here you see that for ESG ratings in our sample, the average correlation is 54% and it ranges from 38% to 71%. 38 is for Standard Poor and MSCI and 71 is for Stainlytics and Moody's, which is also interesting because that means would you graph that in this correlation space, you have sustainlytics, Moody's, S&P and Refinitiv. You have roughly here and then you have MSCI here and KLD here. KLD is not actually really used by investors anymore as a rating. It's a legacy rating. But we include it in our paper because at the time it was heavily used by academia.

Because it was free and on a database that almost every university subscribes to. What's also interesting is that for the ESG dimension, which we will not really look at in the further paper, but here I wanted to show you the correlations. It's actually the highest correlations are for the environmental dimension and the lowest for the governance dimension, which for me in the beginning was surprising, but this is confirmed by other papers now too. The reason why it's for me surprising is because governance has been measured for way longer than ENS Environmental and Social Dimensions. And also it's more, I would say mainstream than the environmental and social dimension. So even traditional investors look at it.

And so I was surprised that it's so low. Okay. Yeah. Okay. Now let me so now, as I said, those ESG ratings, they are constructed where you aggregate different indicators to one ESG rating in order to compare them. We had in this paper to come up with a common language between the ESG ratings and between the indicators of the ESG rating. So basically, we had to map the indicators to our categorization. And here I show you, for example, what we did for the water category. In total, we came up with 64 categories. And here, for example, Refinitiv. In our water category, there is Refinitiv when it wants to measure water. Then they look at discharge in the water system, water recycling, water use. KLD looks at water management and water stress. MSCI looks at water stress management and so on. So this actually we had to create to come up with a common language. And once we did that, we could actually now compare the raters just by looking at those different categories. And this is now a correlation table where we look at and this is pairwise comparisons. So minus one indicates it's negatively correlated. When one says yes, the other one says no in a systematic way.

And one would mean that it's perfectly correlated. One says yes and the other one says yes too. So here I will show you actually just a selection of a couple of categories. It's actually from a robustness check, it's the SASP categories. But in for analysis, we use our 64 categories. It's just easier to present here on a slide. And what you see is that the divergence is all over the place. The measurement divergence, you have a lot of dark green fields for sure, but you also have some dark red fields that indicate there's a lot of disagreement. And yeah, you see for example for Business Ethics in the third line between KLD and Sustainedics, the correlation is 0.1, which is very small. But between Sustainlytics and Moody's 60.8, which is actually fairly high.

Yeah. So there's no real indication why that is and we see that this divergence is really all over the place. What's so interesting also is that there's -50 as you see right minus zero five -50% -0.5 between KLD and Sustainalytics at the bottom in this red field, that means if KLD says this company is a good company, Sustainlytics almost systematically says this is a bad company in terms of selling practices and product labeling, which is basically responsible marketing. So, do you actually target young adults for your tobacco products or your alcohol products? That would be the question behind it. So we see there's a lot of measurement divergence. We also see that there's some scope divergence because some fields are blank. That indicates that at least because of the pairwise comparison, at least one rater does not assess that indicator. So we conclude measurement divergence and scope divergence. Let's look at now at the wave divergence. And here what we do is we use this is a reverse engineering and we regress an ESG raters own ESG rating on its own data but through the lens of our categories. And by that we can actually say if this categorization is a valid construct and we do not lose too much information by forcing their own data into our categories.

And we see that so our measure of goodness of fit is the R2 below and the R2 is actually fairly high except for MSCI. And that means for MSCI because we use here actually ordinary lease squares and ordinary lease squares are linear. So we do not actually really take into account the fact here that all those ESG ratings state that the ESG ratings heavily depend on different industries. As I said, CO2 emissions for aluminum factory are much more important than for office space provider.

And so MSCI seems to have a lower R2 and when I actually just zoom into an industry, the R2 shows up. So the MSCI is actually the only one that really takes into account those industries in a strong way. You also see here that the weights are different. So those coefficients are different. For example, again, for Business Ethics, you have 0.12 for Sustainlytics and 0.059 for S&P Global. That shows us that there's also a weight divergence. So now the real question is how much does actually measurement divergence, scope divergence and weight divergence contribute to the overall divergence? This is actually what we want to know. And we show here we do a lot of mathematics and regressions and I will not get into those details, but we show here that I show you the overall results. And this is where the important arrow is. And we see that measurement divergence is by far the biggest source of divergence. So the reason why those ESG ratings diverge is because ESG raters assess indicators differently and that is followed then by scope divergence. So what actually indicators do I measure? What do I look at? What are the important topics for my ESG rating?

And last, but last is weights with only 6%. And this is then again the relative importance. So again, most important is measurement. And what's so interesting, also from a different perspective, you can say that measurement is of course measurement, but scope and weights is kind of express you kind of express your preferences to them. Because first of all, you decide what to look at and then you decide how important it is. So it is a sort of preference. And this is actually has important implications and I will talk about that later.

Okay. Now, as we saw, measurement divergence is very high. And that also indicates that we do not know how to measure certain things perfectly. We do not know how to measure perfect discrimination perfectly. We resort to all sorts of proxies, as I said, gender, pay ratios, ratios about promotion and others. Here actually yeah, and this is because obviously it's cheaper, it's easier because those variables are very easily observed. But it doesn't really tell you how actually people are treated within a company. If you want to really figure that out, you probably need to put a center on everyone and measure all the conversations or interactions and non interactions inside and outside of the company. And that obviously would create privacy concerns as well. So sometimes you don't even want to measure certain things perfectly because it goes against another dimension.

So then we looked in a next paper about the measurement noise contained in those ESG ratings. And here actually what we do is just to give you an intuition. We actually, look at the standard setting in academia where you try to find out.

If an ESG rating actually causes some change in stock returns or not, if it predicts stock return or not. And there has been plenty of papers written about it. And here we actually said that the problem is that since those ESG ratings are noisy, this link will be a little bit blurred. It's a little bit like this. If I speak like this, you can barely hear me. So now I introduced noise measurement noise. So for you it's difficult to detect what I said and this is the same in those regressions. So there's something that we call an attenuation bias. And so this correlation between financial performance and ESG performance will be lower than it actually is, but there are ways to correct for it.

So, for example, the simple intuition would be an average between that's what you probably learned in high school, but this is only good if the errors are distributed in the same way. This is an assumption here and the problem is that often this is not the case. For example, if you have an ESG rater such as S&P Global, they send out a questionnaire once a year and they only change ratings once a year. So they are very slow moving. Then with TVL True Value Labs, for example, they have a controversy based score where they web scrape everyday news and the score changes every day. That makes the score very reactive, but it comes at the cost and the cost is measurement error. And now if you would take just a simple average, then you can feel intuitively that something not really, it doesn't work. So there's a better way. And so what we do is we use an instrumental range approach. So in this regression of stock return on ESG ratings, we insert a step before where we regress this one ESG rating on seven other ESG raters, or sometimes less it's better. This is explained in the paper and I don't want to get into technicalities.

So now, actually we have a score that we insert in this regression and now we can actually recuperate somehow the coefficient for the true ESG performance. And if we compare this coefficient with the standard coefficient over an OLS regression, we know now the noise that is contained in the ESG ratings, given this setting of a regression of stock return. And so here we show on the left hand side the OLS coefficients and then on the right hand side the instrumental variable coefficients and as a matter of fact an absolute value, they go up quite often. For example, not the first one, but the second one and the third one, and the fourth one, and not the fifth one. It's not significant either, but the 6th one, the 7th one and the 8th one and so on. So out of this 24, I think we have 18 coefficients that go up and quite substantially though, so by a factor of 2.6. So that leads us to the assumption that this link has been previously underestimated by a factor of 2.6 and is stronger.

That also implies that there's a noise to signal ratio of and I will shorten it a little bit just here of 61, as you see below, of 61%. So there is a substantial amount of noise in those ESG ratings, but you cannot say that there's no signal in there.

And this instrumental variable approach, when you do actually then portfolio sorts, this affects them heavily because this is often what portfolio managers do. They create certain portfolio sorts in order to create a portfolio. Just look at the lowest line here, or like the first row actually, where it starts with 1% and goes down to 57.9%. This is the last quintile for the original rating. So the worst performers, and if we instrument them, actually some of those. So if you control for noise, some of the worst performers originally become actually some of the best performers.

Very few, 1%. But as you see those quintiles, they change quite a lot. So the firms in the quintiles change quite a lot. So this instrumental variable approach shows that if we corrected for noise, there would be a lot of movement in those portfolios and they would be constructed differently.

Okay, so this just was just a short way of showing you if there's actually a signal in the noise and what we could do potentially or what we can do to look at it in a better way.

In my next paper, actually, that I co-wrote with Kornelia Fabisik and Zakarias Sautner. I look at actually some other measurement problems of ESG ratings, and there's one ESG rating, one database that actually changes the data retroactively.

And this is problematic because me, as an investor, I buy data for ESG ratings and then I run back tests, and I want to know if I buy that data and now create my portfolios in the past to see if I had started this portfolio before, there would be my strategy works. I can't do that because the data is not the same anymore as it was when it says in the database when they came up with it. So this creates a forward looking bias. And we see that actually quite interestingly here we have three downloads. So basically what we did, we downloaded the data once and then twice and found that the data wasn't correlated.

So we investigated, we downloaded the data again a third time, and again the data didn't really correlate. Here those downloads are the darkest blue one is 2018. The second one is 2020, so the turquoise one and the red one is in 2020, but two months later than the second one. Normally for 2011, there shouldn't be any change for one firm, let's say Nestle, there should be the same score. How do they even get new data? But here for this firm, actually, the rating completely changes. And it's not only a scaling effect. You see, that actually the way the slope changes. It doesn't seem to be a scaling effect. It really seems to be a different ESG rating. So we investigated. And so, as I said, one thing is that here in this space, it's an investor. So ESG ratings follow an investor pay model. So investors buy that data to create their portfolios. And so one thing that an ESG raiters is very interested in is to say that this data can be used as a source of overperformance. So, again, we looked at this regression that I was talking about of stock return on ESG ratings to figure out the impact or the correlation of ESG ratings with future stock return. And for those three downloads, actually this link goes up. But for the same time period for 2011 and 2017. So the longer the way to download the data, the more yeah, the higher this correlation.

So the question now is, does Refinitiv do that on purpose? Do they actually really try to work on this link and they really try to retroactively strengthen the link. When you look at actually at the raw data, just to explain you, what actually happens here is that this is actually before six weeks. So we downloaded the data in February 9 and in March 23 in 2021. So just six weeks of difference. But 80% of the scores of the ESG scores in 2011 were at least slightly different. 80% in just six weeks. When you go to the raw data items, 4% of the companies in 2011 experienced a raw data change in 20 for those six weeks. For me, that is quite a lot. And so we are still investigating why that is exactly what actually we try to find out, actually what the intention, why is Refinitiv really doing this? Because they want to increase, expose their return or the correlation between stock return and ESG ratings.

Okay. Let me just finish the talk with something else. And I want to show you that actually those ESG ratings then matter. Because there's a lot of talk, we talk a lot about them, but then there's also a lot of people that say, yeah, but you know, asset managers, they construct their own scores. So we don't really look at ESG ratings, but they're surprisingly actually funds follow those ESG ratings. So we see, actually, we look at American Mutual Funds, ESG mutual funds, we found 120 over the last eight, nine years. And we look at if ESG rating changes trigger mutual fundholding changes. So do actually those mutual fund managers react to changes in those ESG ratings? And they do. As you see, when the rating changes, there's the gray line at zero or minus one. The holdings actually do adjust as well. With an upgrade, they go up, with a downgrade, they go down. But what's so interesting here is that it takes up to 18, 20 months for those rating change to be integrated in the holdings.

That's a very long time, and especially if you believe that those ESG rating are a source of overperformance. So it seems that it might be just a preference story. People actually might invest in those or in high ESG performance because they have a preference for those companies. And when you see actually on the right. We did the same for return it also takes quite a while it takes 18 to 20 months until returns really go up or down, and it's fully integrated into the market. That also might indicate that actually the overperformance here in this case comes from the fact that people buy high ESG performance and sell low ESG performance. That puts some price pressure on the stocks, and hence you observe those positive and negative returns. So what I want to say here is that, yes, those ESG ratings are actually used by mutual funds, and they have an impact on financial flows.

Okay, let me just sum it up because it's time. So ESG ratings, as I tried to hopefully, I successfully conveyed that, are complex constructs. There are no shortcuts here. What I hear a lot is that, oh, there's so much disagreement between ESG regions, we should standardize them. But standardization will be very detrimental to those ESG regions because, first of all, as I showed you, almost half is an expression of different preferences.

And how am I entitled to tell you what actually your preference is? Is discrimination or climate change risks more important? The other problem is this substantial amount of measurement error. We can standardize something that we don't know how to measure perfectly.

And again, the standard here, also, when we standardize something that we don't measure perfectly, companies will be able to game that, and they will learn how to do that. So here the no standardization is about ESG rating agencies. It's not about firm level disclosure, because firm level disclosure, in my opinion, should be standardized. We should have the same way of accounting for CO2 emissions, but then the interpretation of what this amount of CO2 emitted actually means, and if the company wants to reduce it in the future or not, this we shouldn't standardize, because this we still don't know how to measure it perfectly. But as I also showed, there is some signal in that noise. Thank you.

Emmanuel
Thank you very much, Florian. So, great presentation. So now I'm turning to Abraham. Abraham, the floor is yours. You're on mute Abraham.

Abraham Lioui
Yeah. Thank you very much, Florian. So we have four papers, one presentation. This is super great for us. We don't need to go and look into the papers. So, for the audience, I would like to say that the first paper Florian talked about is really a seminal paper in the very noble understanding of the word seminal. They are the first author who have put on the table this problem of ESG confusion. And since then born, many papers followed up with solutions, discussions, whatever. It's really a seminal contribution in the literature on sustainable investing in Geneva. I like very much the paper also on ESG noise, which is very interesting. It's a bit technical, but very interesting, because this is a very smart way to show that EU taxonomy is not necessarily the right way to go by harmonizing everything. But rather, like Florian said it here, as you see in the slide, harmonization is not necessarily the panacea because of what we are about to measure. And heterogeneity or disagreement may be source of information and at least let most people talk, discuss. So what I would like to suggest is to let Marianne maybe ask her questions and then I will move to the Q&A from the chat. Is it okay for you Emmanuel?

All good, so Marianne.
Marianne, please.

Marianne Toscanne
Thank you, Abraham. Thank you very much, Florian, for your presentation. It was really insightful. My first question will be about the wave of consolidation that we can see in the rating agency sector today. For example, last week, S&P Global has acquired States of Green Business from the center for International Climate Research, which is a Norway foremost institute for interdisciplinary climate research. And so the acquisition of the firm will be integrated into S&P Global Rating. For example, Shades of Green Business has won several prizes over the past few years and is really recognized for the quality of its analysis as it provides deeper transparency on climate risk. So, of course, this is just one example of the many acquisition that rating agencies have made in recent years, particularly with regard to ESG. So my question is, how do you analyze this wave of consolidation that we can see today in the sector, and do you think that it will help to reduce the confusion in the years to come? Thank you.

Florian
Yeah, thank you for that question. So I think, generally speaking, the ESG raters should get more sophisticated about their measurement practices. So one thing I didn't talk about and what I showed in the aggregate confusion paper is actually this halo effect. So we show actually that because often ESG raters or ESG analysts within those raters are organized by firm and not by topic. So they have to look at between 38 and 282 different topics. And it's obviously very hard to be a specialist on child labor issues and CO2 emissions and corruption. And so we see actually that those analysts often tend to if they have a rough idea of what the company how the company performs, then this influences their choice for each indicator. So that means that might hint that there's a lack sometimes of sophistication in each dimension. And so I think it's great that they try to supplement this with, for example, by buying smaller raters that are specialized on just one indicator. The problem here is now that it now becomes a monopoly or an oligopoly, right? And you can see actually, especially in the last six months, there was before there was this tendency for those ESG raters to open up.

But in the last six months, actually, it turns out that they go the other way. And some still try to have that path to open up a little bit more, but some others actually closing down again. And so it is really hard for me as a researcher to criticize those ESG raters as the method, measurement methodologies and find out if they're actually what the quality of this ratings are without actually knowing, really having open books here. And so I think generally so for this kind of acquisition, I think that would be okay. If the regulator enforces more transparency, then. So increases the competition again between those ESG raters by telling them that they have to open their books not in terms of ratings, but in terms of measurement methodologies. When I contacted some of those raters five years ago, they didn't even have a PDF or a PowerPoint presentation to explain to me the methodology, some of them. So just to tell you how opaque that whole field is.

Marianne
Okay, thank you very much for your answer. I have another question related to standardization you were talking about. Do you think that the ultimate solution and radical solution to increase transparency and to increase also this kind of standardization will be to create one and unique rating agency? Because there was a kind of rumor among European regulators a few years ago that wanted maybe in the future to have just one rating agency for all rating.

Florian
Great, great question, Marianne. And I remember that. And I reached out to the regulators and had lots of discussions because I think that would be very harmful because the only thing, as I said, right, so the divergence, as we show, is a little bit less than half as preferences, and everyone is entitled to their own preferences. They also evolve over time. We can actually see that in the last four years, discrimination, especially in the US through Black Lives Matter, became much more important. Climate change issues became much more important in the last years. And so one job of those ESG raiders is to interpret actually what sort of the representative investor wants. And it's good that we have different approaches here because then we can also come to different conclusions and the investors can then actually try to figure out which rating agency they agree with most.

And get that information. On the other hand, measurement divergence is the biggest source, and if you don't know how to measure something perfectly and you just standardize it, yes, of course we do have the same rating than everyone, but we would completely prevent anyone from innovating in that space anymore. And I think innovation is the only way how to improve measurement. So the regulator should do anything he can, she can to improve regulation, improve this competition.

And for me, I think the only way I see you can do that is by opening the books. And this transparency I already touched upon.

Marianne
Thank you very much.

Florian
And this is just to tell you because it's very interesting, because I speak a lot about ESG raters and I speak much less about firm level disclosure. And I see that there's often a confusion around. So I'm very much for the standardization of firm level disclosure, SASB, GRI, ISSB and even mandatory disclosure here, standardized disclosure, because we have to figure out how we calculate, for example, how we report sexual harassment cases, right? In terms of discrimination, we shouldn't disagree on that. But then the rise of sexual harassment cases, what does it mean for discrimination? Does it mean just if for example, in a company the detective harassment cases go up, does it mean that the company performs better or worse? You have to see the context because maybe the company is just becoming more transparent, opens up and so people now can file complaints. So then naturally those cases would go up. But that's actually an improvement. This actually just putting in context and looking at what the intention is of this company. This is actually then the job of the ESG raters. And this shouldn't be standardized, this is an opinion.

Good, thanks. Thank you for those excellent questions.

Emmanuel
Thank you, Marianne. So we got a lot of questions in the Q&A chat box, so I will pick some. Abraham, if you pick other one, don't hesitate. So the first one in term of ESG ratings and returns, do you think that it can be a structural factor, ESG so ESG factor or ESG momentum factor? So I think it's related to asset pricing applications. So what are your views on the ESG factor stuff for designing some investment strategies?

Florian
Yeah, I think here the question is again, the problem is that ESG means so many different things for different people, right? So having one ESG factor means that everyone has also sort of the same preferences and... Yeah, and I think that most likely on the overall ESG level because there's so much scope difference that probably is maybe a wrong assumption right in the end.

But there is actually behind that is the question if also ESG is a preference or risk factor and that is still different, people see it differently, right? Some integrate ESG because they want to improve their risk assessment. Others integrate ESG because they want to cause real world change.

They want to have an impact. And I think here there's actually loads of research opportunities and also the regulator has to actually certain things are not going the right way. For example, SFDR, where you have the distinction between a dark green and a green fund, but you don't really have any mandatory disclosure of how actually the fund wants to achieve impact. And just owning the footprint here, for example, in an Article 9 fund, doesn't really necessarily mean that you change anything in the world. So I think that question still has to be settled and is much more complicated than just if there's a risk factor, ESG risk factor or not.

Emmanuel
Thank you, Florian. Another one. Will the launching of ISSB work ameliorate these issues of flag of tolerance between ESG rating methodologies?

Florian
It will give us better data.

So I think the question is, do. We actually need less disagreement is not the question we should ask. We should ask we need more signal in the noise that is actually how can we achieve this? And I think that standardized data, especially something that everyone can get behind, so ISSB might be a sort of like minimal solution. Right. Disclosure could go beyond that because as they perceive it, the ISSB is very heavily based on financial materiality. And so I think if disclosure could go beyond that, but at least we already get a good amount of data from the companies themselves. If we can actually then everyone get behind it and use it in mandatory legislation, and that would be a great start, because then for sure, the signal would be improved because a lot of ESG raters also impute data, and they don't have to do that anymore

Sorry?

Emmanuel
Thank you very much. Another one. I pick a student here with aspirations to work in the field of ESG rating. Could you please suggest some reliable online resources to find information to start learning about this field? I think your papers are a good start, but you have other advices where to find good information with respect to ESG investing.

Florian
Yeah, I think EDHEC wrote a lot of good stuff about ESG investing, and I'm not saying as a joke, I really do truly believe that because it's very applied what EDHEC often writes about ESG investing. And I think that's a good start.

Yeah, I think it really depends what you want to do. If you really want to be a common fund or portfolio manager, then also, I really like Thierry Roncalli's Handbook of Sustainable Investing. It's also a good way of maybe starting.

Emmanuel
So just for the audience, we do not coordinate with Florian before for this answer. Just to be clear, what will you advise to an asset manager who would like to follow the ESG integration path? I think what source of ESG information do you think is worth considering? I think it's just based on your experience with respect to ESG providers.

Florian
Yeah, so I think what we show clearly in this noise measurement or in this measurement noise paper, is that you clearly have a benefit from using different sources. Because you can use them and filter out a little bit of the noise. Yeah. So I think I recommend reading that paper and maybe using that methodology I will put online in the next couple of weeks another paper that is co-written with also Roberto Rigobon and Andrew Lowe here from MIT and where we show actually different and this is more like practitioner focus, where we show actually different aggregation methods that you can use in order to reduce noise between the raters. But we still have to polish it before we put it online. But yeah, I think what's really important here to just realize is that easier. They collect data and come up with measurements differently and you can use that in statistical ways in order to reduce noise and you clearly have a benefit from that.

Emmanuel
Thanks. Given the range of viable numbers, do you see ESG going into report of ranges like medicine, you are healthy in a range and you are in danger on other range.

Florian
Sorry, I didn't understand that.

Emmanuel
The question is with respect to the use of ranges, with respect to the ratings, I guess. And the student take the example of medicine saying that if you stick in this range you will be good and if you are not in this range, you will be in the danger zone, you won't be ESG compliant I guess.

Florian
Yeah, but this really depends again on the rater because they assess it differently and there's also different preferences and I suppose it also depends on what you make out of it. Right? So for example, some big asset managers say that the whole company is not allowed to invest in the worst 20 players of the whole universe or in the worst 50 for example. And then yeah, then it would be clearly such a range. But we are not there yet and I don't think we will get into since we don't have any standardized ESG rating and I agree with that. I think we can just ultimately say this is a bad performer and this is a good performer. It's in my view a bad performer. It's in your view a good performer but it's really hard to say that this de factor is a bad performer.

Emmanuel
Thank you. From my side, I have something you didn't discuss with respect to your last paper, the one I read on the Economics of ESG ratings. There is some interesting result with respect to the low or no impact of ESG ratings, with respect to the cost of capital and also with respect to the behavior of manager. Can you talk about that? Because there are a lot of papers that try basically to see if there is any impact of impact investing or investing. So I think would be interesting if you can just a bit on this last output of your paper?

Florian
Yeah, for sure. We didn't find any impact on firm behavior. Not much except for when ESG ratings downgrade, MSCI ratings downgrade. It seems that some companies actually want to actually increase the ESG rating after that and they choose the path of least resistance and that's the governance dimension and then the government's dimension often increases after a decrease of the ESG ratings. And by that then they're more likely to hold that ESG rating equal. The interesting thing is that they don't do that in the environmental and social dimension because I think they are much more expensive often and harder to actually it's harder to change things. You can't just change the emission of CO2 emissions within a couple of months. It takes time and it's expensive.

Emmanuel
And with respect to the cost of capital?

Florian
Yeah so we find that there's an impact on the cost of capital because the returns go up over the long run after an upgrade, MSCI rating upgrade. So the cost of capital gear goes down and for a downgrade, the cost of capital goes up, and that by 3.7% for down and 2.4% for up, which is pretty.

Emmanuel
Thank you very much. I don't know if Abraham you want?

Abraham
Yeah.  Emmanuel , can I ask a quick question? Florian, when I looked at your graph about rewriting history, they look almost parallel. My question was what happens in the cross section? So the rate change okay, but what about the cross section? Does this change in the rate change also the positioning of the firm in the cross section?

Florian
Yeah. So we do have quantile analysis like similar that I showed in the measurement noise part, and it actually really affects heavily portfolio sorts. It affects them so much that as you can see, then we showed this regression of stock performance on ESG performance, and the coefficient goes up right. Download after download. And this is because there is actually a change in the sorts, in the portfolio sorts. Interestingly.

So it is economically basically your question is, is it economically meaningful? And it is.

Emmanuel, you're not here.

Abraham
You're on mute.

Emmanuel
It's my turn now. Abraham.

Abraham
Okay.

Emmanuel
I just want to thank everybody. Thank you, Abraham. Thank you, Marianne, and most importantly, thank you, Florian. We finished well this year. Florian, you have a session with the highest level of attendees. We get more than 120 attendees for today's session, more than 24 questions from students. So it's great to finish the year with this session. We will be back in January for a presentation very different from Nick Baltas from Goldman Sachs talking about Systematic factor investing. And in February we will have Jean Boissinot from Banque de France that will talk about Climate scenario from NGFS. So thank you everybody. Thank you very much, Florian, and we are looking forward to read your new paper with Androlo so looking forward. Thank you very much. See you and have a good year.

Florian
Thank you.

Emmanuel
Merci beaucoup.

Make an impact
EDHEC BUSINESS SCHOOL
[Music]

 

[Music]
EDHEC Business School
EDHEC Speaker Series - The future of Finance
Big Data financial applications

Ronnie Sadka
Professor of Finance and Associate Dean od faculty,
Boston College

Tuesday, 15 Novembre 2022
6 P.M - 7 P.M Paris time

Emmanuel Jurczenko
Okay, so let's go ahead. Hello everyone and welcome to this new Speaker Series on the Future of Finance. I'm Emmanuel Jurczenko, I'm the director of the graduated Finance program here at EDHEC Business School and it's my pleasure to welcome you today. So for today's session, first like to introduce our two commentators for this event today. So we have first Gideon Ozik. So Gideon is an affiliate professor at EDHEC. He's a managing partner at Market Media Stats. He's also in fact a graduate of our PhD in Finance at EDHEC. So we are very pleased and very proud to have Gideon today. We also have Thibault Uhl. So Thibault is an MSc student in the MSc in Financial Engineering. And it's my pleasure to introduce now our guest speaker for today's session.

Emmanuel Jurczenko
So Ronnie. Ronnie Sadka. Ronnie is professor of Finance and Associate Dean at the Carroll School of Management at Boston College. Ronnie is internationally renowned financial economist. He has published lots of scholarly articles. I used to follow his research on hedge funds work on liquidity and now we got very interesting research article publication on big data driven investing. He's also a confident managing partner at the same company so Market Media Stats. So we will see if Gideon will be a good commentator with all the insight he got with respect to the technology. And we are very fortunate to have Ronnie today that will talk about NMP and most precisely some application for extracting financial and economic narrative. So without further ado, I'm turning to Ronnie. Welcome Ronnie, and the virtual floor is yours.

Ronnie Sadka
Thank you Emmanuel, it's a pleasure to be here. And I'd like to start off see if everyone can see. There you go. Yeah. So again, it's a pleasure to be here. I love talking about this subject. I think it's very exciting. It's been dear to my heart for more than ten years now and I've been actually collaborating with Gideon early on and all that research of its hedge funds and part of it in liquidity and most recently talking about or developing big data applications for financial markets. So let me give some motivation for why I think this is important and what it is that we're trying to do. So I recently came across something I've thought about, but actually there's a term for it. It's Samuelson, Paul Samuelson's Dictum, which he had made and his point was very simple, that it seems that the market is micro efficient, but not macro efficient. The market pays attention to individual firms, the firm comes out with an earning announcement. A lot of people look at it and a lot of firms are being analyzed very closely. However, the market seems to be missing very large trends. They miss the housing bubble, they miss the .com bubble, so he kind of formulated this informally saying that the market seems to be macro inefficient.

Ronnie Sadka
Related to that, there's the Schiller book he published 2019, very timely, that discusses the importance of narratives in understanding financial markets. And his view is that we should include narratives in forecasting models, in economic models, because if we don't do that, we're going to miss a lot of the movements in financial markets. So the point of this talk today is really to show how we can quantify such narratives. I think the challenge is that people, when they make investments, they think about a narrative, and they often are not able to connect the narrative to the tradable asset. So I don't think that people are looking at, let's say, the value factor and say, oh, the value went up or down. What really they care about is, wait a minute, does this have something to do with the business cycle? So there's something underlying the factors that we're able to measure so far that really drives people's investment. And we call them narratives. And what we're going to do is I'm going to show you how we are able to measure and to quantify narratives and then include them in asset pricing models.

Ronnie Sadka
So what we do is we turn to media coverage. And what I mean by that, so we built this technology to collect information from the web. Think about it as a copy of the Internet that we do every year, point in time. We've been doing it for more than ten years, since 2010, we have point in time information. Every day, build a machine every day goes to the web, collect all the articles, digital media that they can. And we're talking about more than 150, it's about 200,000 different sources we tap into every week. It's about 2 million media items that we collect every week. And what we do is, first of all, it's a lot of data. And when we started this project, we start looking at the data, and we realized that there might be a lot of biases. So the first thing we do is we take a look at the data and we try to correct for a lot of biases that we found. And a lot of them, I think, are very interesting. For example, sources, journalists have incentive to publish information, but it doesn't mean that all information to publish is new. It's not new information. For example, we find evidence that suggests statistically something like you can interpret it as IBM is mentioned in the 3rd Wednesday of every month.

Ronnie Sadka
We find this kind of periodicity. Now, we think it makes sense because maybe it's consistent with journalists having deadlines. And Tuesday night there's a deadline. The editor comes and asks the journalist, what do you have? Nothing. Say, well, write about something, because we have to generate some information tomorrow's paper. So they write about a firm that they follow. Maybe they follow ten firms and they know them closely, and they write about something. It doesn't have to be necessarily new information. So we try to correct for this type of bias or coverage bias. There's also bias in terms of geography. So when a firm is covered, let's say if Microsoft is covered by Seattle PI, it might be in a different tone than if New York Times is covering that. So we actually find that and the more distant sources from the headquarter of a firm are more impartial. Again, we mentioned there's calendar effects, so there's Monday News, which is some firms that are also always mentioned on the weekends. There is a length of article bias. I thought it was very interesting, the longer the article, the more the sentiment converges to neutrality. Because when you have a long article, the journalist says, well, on the one hand this, on the other hand that. So they're trying to demonstrate both sides of the argument. So then at the end of the day it converges to neutrality. And so when you look at short articles, even titles might be more informative than long titles. Again, so there's a lot of these biases we try to correct for them. And I want to mention again, we're looking at all sorts of media. So it's not just general press, it's going to be articles that come in from specialized magazines like Pension & Investment Magazine. We have press releases of company. There's some social media that we're going to talk about towards the end of the talk. So we try to organize all the information we have into different reservoirs of information. And by doing so, we're able to correct for a lot of these biases. So we have a reservoir for all articles that talk about come from the general press or to talk about general topics. We have all articles talk about currencies oil articles, talk about corporate communication, about corporates, all articles that talk about country equity, all international articles, all politic driven article, et cetera. So we try to organize things and information in reservoirs and then we go back to the well and measure different narratives.

Ronnie Sadka
So how do we measure narrative? So the solution we have is actually quite simple. What we do is we look at our reservoirs of information. Let's say there's, let's say a narrative that is emerging and I'll talk about what it means emerging. But let's say COVID-19, well, you can go and measure how many articles in a particular reservoir mention COVID-19 and what you see here on the graph, the top graph, you can see the time series of the percent of articles that talk about any given narrative. So when I talk about narrative, again, there's a lot of different types of narrative you can think of. So you can think about a narrative that talks about like a stock market or VIX, which is like a price narrative, or GDP growth. GDP growth is not exactly price, but you can think about price narratives and you can think about non-price narratives like COVID-19, Brexit, trade war shortages, et cetera. And you can think about evergreen versus emerging. So COVID-19 is emerging, meme stocks are emerging, but evergreen are like inflation and recession and GDP growth, et cetera. So these are more evergreen. So you can measure there's all sorts of narratives, but all of them you can measure in the same way of calculating the percent of articles that talk about the particular theme as a fraction of all articles in a given reservoir.

Ronnie Sadka
So what you see on the top again is you can see the discussion of COVID-19 at some point. If you can see my cursor around early 2020, you can see that about 75% of all articles were talking about COVID-19. So the only thing is you can quantify every day how many articles and you can do the same thing for inflation. You can see how inflation was time varying, but then it started trending around 2021. The nice thing that you see here is that inflation is not just discussed in the last year or so. There's a time variation in the discussion of inflation. You can see in 2018 there was another discussion of inflation. And the nice thing is that the beauty of looking at the narrative is that although inflation did not materialize in 2018 and CPI didn't change much, there was significant discussion of inflation and we're going to be using that for pricing model. I'll talk about that. And then more recently armed conflict. You can also see the jump in armed conflict in the beginning of this year and that discussion has been more moderate in recent months. If you look take a closer look at COVID-19 I like to use this example because I think it demonstrates how information propagates through the market, right? So you can see here the discussion of inflation, the inflation narrative, and you can see it over different reservoirs.

Ronnie Sadka
So what you can see if you only look at articles that come from FX Traders like trading magazines and FX Traders talking about, you can see that the jump in COVID-19 discussion first happened in the FX Reservoir. So traders around the world, they talk a lot about what's happening and they're the first to discuss significantly in a significant manner the COVID-19 that's actually with early it was the second or third week of January that they started discussing that after following that you can see corporate articles started talking about COVID-19. Following that there was the more general press. So the general press received after and last of the game are articles that talk about politics. The interesting thing though is within a month it became the most discussed in politics related articles, which I think if you look in the United States, certainly it became like a polarizing event. People really defined which side you are in and became a lot of discussion of COVID-19 in politics.

Ronnie Sadka
Overall, we see that the trend in discussion has come down, but again, around the election of 2020, again October, November 2020, again you can see significant discussion of COVID among the politic-related articles. So again, that just shows you how you can get insight from understanding different reservoirs and what they're talking about.

Ronnie Sadka
So to better understand if these narratives are important for financial markets, just as Schiller hypothesized and discussed in his book, we did the following analysis. So this is, again, just trying to look at the data. We did the following thing. We graphed a few narratives in the space of intensity. So intense discussion on the narrative. How much discussion is the narrative? And that's on the vertical axis and the horizontal axis is the R2 of you take the spy and you regress on we did this with weekly data. You regress on changes in the narrative. So again, the narrative is a quantified number, right? We have that every day. We can look at it over a week, and then we look at changes in narrative from week to week. We can run a regression of SPY on the narrative, and we can get an R2.

Ronnie Sadka
And then we can plot in the space of discussion, like intensity versus R2, we can plot all the narratives, and we can see which narratives are highly discussed and influenced the market, and which narratives might be highly discussed, but they're not really influencing the market. Or which narratives are not really highly discussed, but they have a significant, profound impact on the SPY. So you can see, for example, market crash is in this quadrant, this first quadrant. Those are highly discussed and highly affecting financial markets. You see Interest Rate and Federal Reserve and Inflation, Market Crash. All of those seem to be highly discussed and very important. Again, this was updated two months ago. I'm going to show you a few things that have been more recently updated. But this is just to show you an idea of how we think about narratives and how we think about we call this the narrative map. So it shows the narratives that are in discussion and how much they're affecting financial markets. Asset derivative, international organizations, they're not highly discussed and not affecting financial markets. Biden, Bitcoin are not highly discussed, but they're more on the trend of actually influencing markets more than the average.

Ronnie Sadka
So that just kind of shows you the map of how we think about narratives and whether they are important. One thing I can't show you here is the path. So sometimes you can see how narratives are emerging. There's some discussion and then there's kind of more discussion, but it takes time until they affect the market, and then this only affect the market. So we can track these narratives, and then at some point after they affect the market, then maybe there's less discussion. So there's a path and we can actually see that dynamically. So that's explained a little bit about how we think about narratives and how we quantify them. And now I want to take you to some use cases. We can talk a lot about how we collect information. There's a lot of NLP that goes in, there a lot of that technology of how to collect the information and define narratives. But what I want to do now is kind of move straight to use cases and we can delegate all other such questions to the Q&A.

Ronnie Sadka
So I'm going to show you a few things. One is I'm going to show you how we create an index. And I'm going to show you a recent example that we partnered actually with MSCI to create an inflation index. I'm going to show you how we did. Also, ESG is a big thing, and I know there's a lot of emphasis on that at EDHEC as well. So I'm going to show you how we can add to the discussion using our narrative based, media based narrative. And then I'm going to show you some other applications based on rotation strategies for themes. And after that I'm going to talk a little bit about social media.

Ronnie Sadka
So let's start with the first use case, which is building an inflation index. So here's the challenge I want you people to understand challenge. People have looked at inflation forever. It's very hard to get an equity basket or stocks to find stock's exposure to inflation. Why? I mean, if you just run a regression of stock returns on CPI, you're not going to find much. As I showed you before, CPI didn't really change much over the last ten years. If you take out the last year and a half, didn't really change much. So you're not really going to get a good measure of exposure firm to CPI.

Ronnie Sadka
So that's one issue. Now another issue, of course, is that there's issues with measuring inflation and measure expectation to inflation, et cetera. So our solution is the following. Let's try to interact inflation related variables with the attention to inflation, with the attention to inflation. So here's, take the following example. Let's look at oil prices. People used to say, well, if you think about inflation is coming, you're going to invest in oil prices. But what's the issue with oil prices? That oil prices can move around irrespective of inflation. Something can happen in the Middle East. OPEC can decide to cut the supply and then prices are going to move around. It might not have anything to do with inflation. It happened a few times in the last decade, but it didn't really have anything to do with inflation. So what are we going to do? We're going to regress stocks on inflation, on oil prices. It changes, but we're going to interact it with the attention that the media gives to inflation. So what we're going to do there, if you look at this equation, this model, you have returns of a stock and we're regressing it on the market just to get correct for beta exposure Capital Beta, but then we're going to regress it on, say, some factors related to inflation, oil prices for example. But then we're also going to interact it with IA, with this inflation attention and what that does, it's able to identify when a stock moves around with oil. I want to record the beta only in periods where there's actually attention to inflation. So people are pricing securities all the time and they might be pricing securities with respect to oil. I want to identify which assets have a high exposure to oil. But when people are pricing that exposure, when they in their minds, they are thinking about inflation. So by using that interaction term and using that kind of beta, I can identify which stocks move around with respect to oil, but only in the context of inflation. And I'm going to do that with gold and I'm going to do that also with inflation break even. So for all these I'm going to actually look at the interaction term and I'm going to use that to sort stocks according to high exposure and low exposure to inflation. By looking at that interaction term, I'm also going to add some direct media factors that we've used.

Ronnie Sadka
We look at Fed policy and we try to look at how much of the Fed policy discussion is related to inflation and we also look at inflation direction. So we look at our media reservoirs and we see how many we have articles that talk about inflation increase versus decrease. Okay, so I'm not going to get into too much detail there, but again, it's all based on our media technology to assess or to quantify narrative. And we also used them in the model. So at the end of the day it's like a five six pillar model where every stock is being ranked according to either these variables with inflation related variables interacted with attention to inflation or these media direct media factors. We create a ranking per pillar per stock and we add them together. There's a combined ranking which we call MIS. And so that's an overall sensitivity of a stock to inflation. We do it in a rolling basis. So every month we have a new ranking for every firm. And on the graph here below you can see the underlying inflation tension variable that we use. What are the results of that? Well, you can look at the portfolios that arise from taking, let's say we look at their large cap firms, the MSCI firms, it's about 600 firms and we divide them into ten groups.

Ronnie Sadka
And you can look at the upper decile, the lower decile according to our MIS, the inflation sensitivity index. And what you can see is what you have here on the left is a plot of the correlation with the actual CPI of each of these ten portfolios for all periods, but also looking at only the periods where there's high attention to inflation. As you can see that the top portfolio, the number ten is the highest correlated with CPI. When there's a lot of attention to inflation and portfolio, one has a negative correlation with CPI, which is exactly what we're trying to do. We're trying to create here an equity basket that will have exposure to inflation. Inflation goes up, the equity basket return is going to go up. When it goes down, it's going to go down. On the right hand, you see a plot of it's a twelve month rolling CPI versus we looked at the top decile portfolio, excess return, excess of the average MSCI index. And you can see there's a really nice fit, relative fit between the changes in the CPI over a twelve month period change CPI and the portfolio ten, the top decile return.

Ronnie Sadka
So the point is with this technology and understanding what the people price securities when they care about attention, that enables us to create a portfolio that would track really well. One of the most difficult things, I think to track, which is inflation. And if you do this, we did some analysis, if you don't use media attention, you just use standard models, you don't get as nearly as high correlation with CPI. So with our model, you get anywhere between 60 to 80, 85% correlation with CPI. And without a naive model, without media attention, you get less than 20% correlation.

Emmanuel Jurczenko
So Ronnie, did you try obviously to have some edging strategies and to look to the edging performance of this kind of factor or factor mimicking around this beta portfolio?

Ronnie Sadka
So you mean like what you want like a ten minus one?

Emmanuel Jurczenko
Hedging the inflation risk, basically.

Ronnie Sadka
Yeah. So you can do this. It's two sides of the coin. Some people want an inflation exposure. So if you think you're going into an inflation period and that's the index we created with MSCI, how can I protect my portfolio when inflation is going to go up? You invest in this D10 portfolio. So that's exactly it's a long only and that's exactly what we've done here. You could decide that you want zero correlation with inflation, in which case you can actually regress your assets. We could talk about that's a different way to do this. You can regress your assets on inflation and then kind of tease out any correlation with inflation. So you can try to orthogonalize your portfolio to any narrative. So that's again, the other side of the point.

Ronnie Sadka
So moving on to the next application is ESG. So what have we done here? So many people know, including Emmanuel, there's a lot of different measures of ESG. There's the MSCI measures, the S&P measures, and there's other measures in academia that we came up with, and we think all of them have a similar challenge.

Ronnie Sadka
But our solution so instead of looking at fundamentally either financial statements or SEC filings, or looking at fundamentally in each company, we took a different approach. We measure the ESG as a narrative we measure for example, carbon emission or anything related. There's a few sub narratives but you can think know climate change or carbon emission as narratives related to ESG. And what we've done is we created that narrative and we can calculate firms exposure to the narrative by just running a regression of firm return on the intensity of discussion of climate change. So you can do it both. There's different ways you can do it with intensity. So looking changes in intensity we can look at changes in the sentiment with respect to climate change. Both of them, we put them together. Again, there's a process to do that and we can get a score for each company. So what that climate change beta would do is would capture firms that their stock price goes up in period where there is a lot of discussion about climate change. So let's say the media talks about or people talk about globally about how the climate is doing worse now, okay, that's what people's mind.

Ronnie Sadka
So what we try to observe in that period which stocks are winners, which stocks are losers, okay? So by doing that on a rolling basis we can find a beta estimate and a high beta would mean that the firm does well when ESG discussion is more negative and more poor. So that to us sounds like a firm that is creating a solution to ESG. So the firm does well. If people really think climate change is bad then they think oh, this firm is going to thrive. It's probably a solution if the climate becomes worse. So what we do is we classify these firms into environment-friendly, environment solution companies versus those that are not. And then we look at here we have MSCI firms, so here they have MSCI firms and we look at a subset of firms that we deem high climate-friendly and you can see that exposed, you can see that there's higher returns for that subset. You can do it for what we call carbon emission solvers. So this is again an MSCI measure and within the solver the firms that they define solvers. If you look at only the subset of firms that are also climate friendly according to our measure then you get a higher spread.

Ronnie Sadka
And it doesn't matter if you look at the carbon emission solvers or ESG industry leaders according to their definition, you get better performance. And you can see the performance here is charted in the bottom graph. So the point is to say the following that this technology or this approach is trying to understand it tries to understand what the market believes a firm value is in different periods. So when there's more discussion about climate change and the firm goes up, it suggests to us that the market is telling us that this firm is more environmentally friendly. So that's how we use kind of this narratives to understand market based measures of each narrative. We could do it for many, many different narratives. We track more than 800 narratives in our system.

Ronnie Sadka
Okay, another example, megatrend rotation. Again, another application that we've done with MSCI. And MSCI has these 24 megatrends, so they look at different things, like AI or technology, smart cities, aging. There's a lot of different megatrends they look at. And what we've done is something very simple for them. We just looked at the last quarter, each of these megatrends, we can use NLP to understand what is hot in the media, and we could look at what is the media saying about each of these 24 megatrends every quarter.

Ronnie Sadka
And so we can look at the end of the day, what we look at is which megatrends are being highly discussed in a positive manner, both of them highly discussed in a positive manner. And we end up investing. If you look at a rotation strategy that ends up investing in the top six megatrends every quarter, you get a really nice performance. So, again, the idea here is to identify which megatrend. These are evergreen megatrends. We're trying to identify which megatrends are being hot, and we think there's a short run continuation. So we're able to capture the hot megatrends. If you invest in them in the short run, you get higher performance. And we find that this is very, very low correlation with momentum. We just run a momentum strategy with this 24. It's going to be underperforming. And there's really zero correlation with the attention-based measures that we have. If you look, in general, not restricting to these 24 megatrends, we look at many, many themes, and I'll try to show you if I have time on our web app that we created. We track many, many themes and we create baskets for each theme.

Ronnie Sadka
If you create a strategy that always invest in stocks that are highly sensitive or highly exposed to the top themes at any given point, again, you can get a really nice kind of strategy. It's like a narrative or thematic momentum in themes, strategy, very low correlation with anything else. And this is just an example out of just a few weeks ago, and the top 15 or ten narratives were space exploration, student services, water stress, et cetera. For each of them, we have a set of about 40 to 60 stock names. If you invest in all of them, and we just did something simple, equal weighted for one month, and then you kind of rotate that point in time every month. You can see this graph here on the bottom right? So again, it's kind of interesting we think that there's something there.

Ronnie Sadka
Let me talk a little bit about social media and kind of turning to a more academic question here. We've been looking at social media for a while. We actually have a contract with Reddit, and we can drink Reddit information and try to understand what are the narratives that come up in social media. And one thing that think a lot of people's mind know, how does this affect financial markets? I mean, the fact you have social media. And when we think about early on around the pandemic, Gideon and I and co-author published a paper at the Journal of Financial and Quantitative Analysis that shows that in the early days of the pandemic, when there was a lockdown and no one had anything to do there's no sports, nothing going on, people just traded. And what we saw with Robinhood accounts, we saw that there was more trading in stocks around that area and we can pinpoint in regions or states in the US states that came into lockdown, there was more trading of the local firms during those times. You can really do identification there and we find that retail investors really supported market liquidity during that time. On the other hand, we noted in the paper that now with easy access to trading, but also if retail investors start communicating together, they might act as one force and then it might actually create some more risks in the market if there's systematic trading or coordinated trading by retail investors. And then we show that in this paper. So we looked at rated information and what you have here is a graph that shows two things.

Ronnie Sadka
One is the red line just shows you how much discussion there is, the average discussion and that's going to be here in the red. So you can see about let's say 60 posts per firm on average. And we look here at the Russell three per firm on average over time. You can see how that moves around. There's quite a bit here around the Meme stock, the GameStop episode and then the blue one just shows you how many firms in general are mentioned on reddit in any given point. And you can see on average every day there's about 300 firms. It went up and now it's kind of flat at about 300 firms that are mentioned on reddit in the Wall Street Bet subReddit every single day. And theoretically I think there's two arguments here. One is, well if there's more social media and more discussion, maybe it's more noise trading. So from the academic literature of liquidity, maybe what's happening if there's more noise trading, it's going to improve liquidity and there's more people to trade with. So if that's the case, then prices are going to be more informative. So the price is going to more reflect fundamental value.

Ronnie Sadka
On the other hand, if they work in tandem and retail investors might move together because of noise, then suddenly it might actually distort asset prices. So there's these two arguments and what we're going to find, we're going to measure liquidity, we're going to measure earning response coefficients and what we're going to find actually is there is something to the argument that says that there's the more social discussion, then there's less price informativeness. And the way we show that we do it in different ways in the paper, the graph that I like the most is the one you see here on the bottom.

Ronnie Sadka
What does this graph do? Well, we do is we run an earning response coefficient that's really well known in the account literature. What it does is you run regression of returns on the earning surprises. So you have a measure like standardized unexpected earnings. It's really well known in accounting. You look at what earnings are relative to the median analyst forecast and then you look at a three day return around the earning announcement and so you can run a regression of return around the announcement on the announcement surprise, the accounting return shown that is a positive coefficient.

Ronnie Sadka
That is to say, when there's new information in the market, the market responds, the price responds quite quickly. So that's evidence of market efficiency and the market works well. Now, the accounting literature also says that the higher the earning response coefficient, the less price informativeness before the announcement. So if there's people don't really know a lot of information before the announcement, the announcement comes out, all the information comes out there, it really significantly affects stock prices. So the interpretation is the higher the earning response coefficient, the less price informativeness before the announcement. What you could see here after, we have information from Reddit from October 2000 and what you see here, the red are the earning response coefficient of firms that are popular on Reddit and the blue are those that are not mentioned in Reddit. And what you can see is the earning response coefficients are much higher for the Reddit firms. So that suggests that the price informativeness before the announcement is lower. And again we measure whether firms are on Reddit and that month before the announcement. The interesting thing is when you look at these same firms that are highly on social media versus not in social media, when you look before Reddit became popular, there's no difference.

Ronnie Sadka
So it's like a quasi-experiment here where if there was any other characteristic that could explain differences in ERCs across the firms, it doesn't seem to be the case that it can explain why you see differences in ERC after Reddit was introduced versus there was no difference in ERC before Reddit was introduced. So this is to us like a nice evidence to show or we tried to identify that it indeed Reddit and the social media caused less price informativeness in stocks, that there's a lot of social discussion.

Ronnie Sadka
We did this also in another way by looking at looking at anomalies. So there's a famous paper in 2014, JF of Rob Stamba and he looks at eleven anomalies. Since then there's many more papers talk about anomalies and now people talk about the anomaly factor Zoo and 400 anomalies, et cetera. But we just look at eleven anomalies here like momentum and value and quality and all sorts of eleven anomalies they look at. And we have a score for each firm. And on average we know that overpriced securities typically tend to underperform. So when you run Thelma Macbeth regressions of returns on the score that Rob Stamba has, you see a negative coefficient.

Ronnie Sadka
However, when you interact that with social media, you find actually a delayed reaction. So actually these are the coefficients of social media discussion times, the overpricing score. So while overpricing on its own is negative, when you interact with social media in the first, almost like ten weeks post formation, you see this pattern here. So actually firms that are overpriced don't get corrected quickly when there's discussion in social media. If you translate this into kind of a strategy, you can look at overpriced with social media versus overpriced without social media, and you can translate this into a trading strategy. So again, the point of this is to say, and we use some other type of analysis here, we don't just look at NLP, we look at emoji analysis. We start looking at the emojis and we look at the Yolos and the rockets and the moons. And my son had to explain to me what these emojis are and then Gideon explained to me, but at the end of the day, what's happening here is you see all these emojis flying around and I think people just don't understand the information. And then what happens is people start trading all these assets and it creates a higher bound on what the true price, a higher window on what the true price should be, and that creates a price distortion.

Ronnie Sadka
So that's the kind of the idea behind that paper. How am I doing in terms of time?

Emmanuel Jurczenko
I think that the budget is over.

Ronnie Sadka
I'll use the budget later questions to show the application. But I want to conclude, I want to make sure that we're on time on the same page. So what I try to do here is to explain our approach for first quantifying narratives. So these are narratives that could be intangible, but using our approach, we can model them and get a number every day. We can quantify that. Once you quantify that, you can actually use that for applications calculating exposures and betas. And there's a bunch of applications that I showed you here and I wanted to mention that we have many, many hundreds and almost 1000 different narratives. And I guess I want to put a plug out to social media, which is another reservoir of information we have. And we also use that for various applications, either risk or trying to understand short squeezes. And if everyone is talking about a squeeze or rocket, rocket on a stock that has a lot of short interest, that probably presents a risk for the stock. So there's other applications we use with social media as well. And I'll stop here.

Emmanuel Jurczenko
Thank you very much, Ronnie. Fascinating what you guys are doing. So now I'm passing over to Gideon. That would be followed by Thibault or questions.

Emmanuel Jurczenko
So Gideon, the floor is your now.

Gideon Ozik
Well, thank you so much, Emmanuel. Thank you, Ronnie, for taking the time and showing us an example of how rigor academic research could be then translated into practical application. I think the students here definitely appreciate that. So the first question I have is sort of like topical question. I mean, we opened our news sources over the weekend and we were surprised with the enormous fall of the crypto exchange FTX. What's your view on that and how, from your perspective, you'd expect to learn something from the tool and otherwise about this type of breakdowns in the system and particularly pertaining to cryptocurrencies?

Ronnie Sadka
Well, look, it's a great question. Let me actually take this time to in order for people can see the web page I have here in front of you the app that we looked at that we created in the company. You can look at crypto media and you can look at where is that social media dashboard and you can see the negative discussion on crypto. You can already see that. And that's been going on for a while. So I'm not sure exactly if that's going to pertain exactly to FTX, but I want to say that we were able to track the high intensity or negative sentiment towards cryptocurrencies far before the collapse had begun. So I think that's one thing that certainly we could do with our system.

Gideon Ozik
And then as a follow up, do you have any sense of whether traditional media or social media could be better lenses through which to look at this kind of event?

Ronnie Sadka
I think it's a look, again, that's a very good question. When you look at GameStop, we saw that social media discussion, this started about ten days before the general media talked about it. So I think for assets that are depending, I guess, on the audience for some assets, certainly, I think for cryptocurrencies, the social media will give you a much better insight because I think it's more likely that those who are trading this kind of securities are tapping into social media to get some of their information. But I also want to mention that we still don't again, I don't want to get into too much my own thoughts about this, but I think the crypto market, a lot of the movements are really coming from kind of behavior. If we don't know the underlying fundamental behind this, we don't really know how to price these coins. And so I think they're much more subject to movements that are due to kind of behavioral elements much more than fundamental pricing. We don't have a fundamental pricing model for cryptocurrencies. Therefore, I think a lot of the volatility is driven by sentiment. And so I think these measures, precisely what you see in front of you and precisely coming from social media can have a much better chance explaining volatility in these markets than traditional media.

Gideon Ozik
So I have another more technical question and then perhaps one that is more general in terms of the technical. You mentioned in your talk, the ability to measure the intensity of different discussions. And you showed one of the slides, if I recall, is you call it narrative map, where you use that measures of discussion intensity to infer something about what's drive market and to what extent people actually pay attention. I don't think I heard a lot about whether that's the most important thing or actually looking at a specific article and measure its tone, not whether people pay attention to it necessarily, but when they do, conditional on paying attention, whether there's more positive or negative emotions expressed in the text. So I kind of wanted to get your view insofar as kind of how do you think about the first versus the second moment?

Ronnie Sadka
It's a great question because I think a lot of people and a lot of the profession has gone through the path of finding a better professionalizing, the NLP. So trying to understand better natural language processing. And we've so some people use the Harvard Dictionary and then there was the IBM Watson and people had come up with their own thing. And my view is that with the end of the day, if an article is positive, you're going to get it as positive as negative. What's more important is really to understand the biases in the media. That's actually far more important than understanding if an article is positive or negative, because the Wall Street Journal is a negative sentiment journal, because negative new cells and we see it in the data. It's much more important to understand the fixed effect than getting a precise measure of an article's sentiment with respect to a particular stock. I'm not saying the directionality is not important, but I think a better way to understand directionality is to measure what we call these narrative data. So what we look at is trying to understand the intensity of the discussion. So we see if the discussion, if there's high discussion on, let's say, inflation or high discussion on armed conflict.

Ronnie Sadka
And another thing we do is we look at how much negative or positive discussion there is overall in the reservoir about the narrative. So you can see if there's a lot of, let's say, negative discussion about armed conflict, we're going to look at the changes in that and calculate beta. So then we understand the directionality of the overall narrative and then we calculate beta of the stock with respect to this. And so if it's a positive coefficient, that's going to tell us the stock does well when there's a lot of negativity about armed conflict. So that's going to give us the directionality from there, not from whether the stock is mentioned in an article that was negative or positive. So it's much more of a systematic like a systematic asset pricing, factor pricing approach.

Gideon Ozik
And I know that we're a bit short in time, so I'm just going to finish. I'll cut a few of the questions that I plan and I'll ask you a bit about since we talked about the academic research and publications one hand, and then applying it in practice and sell it to the marketplace in different forms. I wanted to get your view of how do you view the competitive landscape in terms of what you're doing, both on the academic side and the competition that you face by sort of in the marketplace.

Ronnie Sadka
Look, I think in terms of competition, there's a few firms out there that have been there for a while that I've heard of and think people heard of them, the RavenPack of the world. I've used even their data and Alexandra, etc. I think one thing that makes our approach different is that we don't just look at the data and just give people raw data. What we do is really give some curated solution. We don't focus on a stock specific thing. We look at a narrative. We try to quantify narrative when we're trying to understand how the firm is related to a narrative. We don't just give people the data and positive sentiment or negative sentiment of a firm and then going to go figure it out. So I think one thing this differentiates us is this focus. Instead of a firm or asset specific, our focus is understanding and quantifying the narrative. So that's, I think, first and foremost, makes us a very different discussion. And then I think our background makes us more suitable to because we've been on the other having been on the other side. Then you can give better solutions.

Ronnie Sadka
That almost it's like almost you create the baguette. Okay, can I use, like, a French thing? Okay. It's like, oh, you put the baguette, but you just have to bake it a little bit. Okay, so then we give it almost ready. And then you put it needed to put it in the oven. We don't just give people the sugar and the flour. We actually just do almost everything, and then people can plug it as they wish. So I think that's in terms of the competitive landscape, business wise, I think in academia, I think what we're really pushing is this attention pricing, which I don't think people looked at at all, but just because of time. We never really got into it in a full fledged paper about this. But I do think that people have been focusing on the wrong things. They've been doing factor models with numbers because they can quantify them, but they're not really looking at the underlying narratives that is driving the macroeconomic factors they're using. I really like the Chen, Roll and Ross paper, the 86. To me, it's a seminal paper, Journal of Business, and they look at asset pricing with different macroeconomic variables.

Ronnie Sadka
But what we do. As I showed, CPI is not a great measure, etc. And I think by looking at people's attention to the narrative, that gives you a much better chance to understand security prices than other numbers that are kind of easily measurable, but not capturing the underlying economic motivation.

Gideon Ozik
Yeah. So thanks Ron and I want to move it to Thibault to take the stage.

Thibault Uhl
Thank you Ronnie, for your presentation. It was really interesting. So I know that now you are now working in a company focused on NLP. And as there are many business students listening here, do you know what are the skills necessary to work in a quantitative field such as yours and can they be deployed by MSC students of quantitative finance?

Ronnie Sadka
Yeah, I think the answer is absolutely yes. Look, I think we're getting to a situation where the world is not just siloed. You need a few sets of skills. I think you need good statistics, you need good understanding analysis, you need good understanding of economics and markets. I don't think you just do it's not just an engineer or an economist. I think the power is really blending them together because with big data you need to know what you're searching for. So I think you need to be somewhat economically motivated and hypothesis is motivated, but then you need the tools to actually perform the analysis. So I think actually masters finance, people with a background, with the right tools could do really well in this. And our company, we've hired people that are engineers with some affinity to finance, etc. So I think this whole idea of data analysis, data analytics, business analytics is the way to go. And again, it's not only finance applications. I think finance and the markets are always good in catching the first trend, but there's going to be other applications in other consulting firms, economics or in marketing, etc. I think the world is becoming more digitalized so you can use the same tool to think for a broader set of applications.

Thibault Uhl
Thank you. If I can follow up just with another question. So NLP is quite narrative. Investing is quite recent now and however, it's kind of growing in the heart of investors and researchers, but it's still marginally used in other business units. So do you see it like propagating past research and asset management through other departments such as sales and trading, or for example, more private structures like private equity in the future?

Ronnie Sadka
Sure? Look, I think yes. So you're saying, well look, even among finance you have the asset managers and then you have the sell side, the trading desk and you have the deals in private equity. I think absolutely, I already see it. It propagates from sophisticated trading and intraday trading and doing quick NLP and all that that we saw maybe more than ten years ago, propagating to more what we're doing like this basket trading, doing this with more Delta One desks. I think they're starving for new content and understanding how to build portfolios for esoteric exposures that clients may want. So I think that's something that's certainly there in the private equity side. Actually, I see a lot of discussion there. I think the problem is the data, but there's more and more data. And I actually had a conversation with a private equity firm about a month ago that their whole specialty is just data, data, data and they try to get any data they can to help them with the decision process. So I definitely see that and I see more people getting hired to private equity firms. So while maybe ten years ago, the only way to get to a private equity firm is being on the sell side for a long time and then you kind of switch to the buy side.

Ronnie Sadka
I actually see that there's more direct people going straight from kind of this data science oriented disciplines to these type of private equity firms.

Thibault Uhl
Thank you. Thanks. I don't know, Emmanuel?

Emmanuel Jurczenko
You can ask.

Thibault Uhl
And how do you see actually quantitative investing evolving? Like, what are the challenges that you're facing now with NNP in asset management and what are the possible progress you can do?

Ronnie Sadka
Look, I guess one thing that we need here's, one thing I think is a challenge, not related, but a little bit more tangential. So I think one thing that we see is a trend with machine learning for example. We've looked at that we actually hire people that that's what they say they do, machine learning. But I think you need to be really careful about that. And then people start using supervised versus unsupervised. I think you just need to be careful that the way I see what we're doing is what we're adding is we're adding a new type of data, a new type of dimension, but still everything is economically motivated. We try to explainability is very important for us. We're not going to just invest in a stock or if the loading is this positive or negative and we don't really understand why it is. We try to avoid that. Although with machine learning, you don't even know what you're getting. So I think that's something that I really caution people to do because it could all work in sample and when you go out, it's not really out of sample. I'm not sure that you should be so comfortable and so confident with results that are in sample based on machine learning that you're not really sure what's driving things. So to me I've seen that as a trend and I worry about that a little.

Thibault Uhl
OK, thanks.

Emmanuel Jurczenko
Okay, thank you. Thibault. So there are a couple of questions that have been sent by students. I think one question, obviously that I have the same one in my mind that means can you say a bit more about the construction of the indexes keyword base? You know, because you have narratives right. So my understanding one way to obtain is in one article you mentioned that you have a lease coming for the Journal of Economic Literature that you augment with okay, but the thing to measure the intensity, that means there might be words associated to the narrative. So can you tell a bit more about what is behind that? You were talking about machine learning. So there are some interesting papers that are working with topic based approach, LDA, this kind of stuff. So what you guys are doing, not doing this with secret, but a bit just to understand how.

Ronnie Sadka
Yeah, let me get some context. Early on, we're also as a firm developing and the technology moves pretty fast and we try to be on top of it. So at the beginning we did looked at just kind of keywords, a keyword approach. I think that serves us well in the sense that I think we're able to measure the density pretty well. I think if people are talking about a subject with all the words that are related to that, I think you're able to understand if the topic is being discussed. I think the challenge, what we've been looking at more recently is more kind of word like embedding. So then you start understanding which words should be next to other words. And we try to understand narratives that way. That's especially important when you try to understand emerging narratives because how do you understand whether you look at something like COVID or meme stock that were not really present before two or three years ago? How is the data telling us in a systematic fashion that there's something new? And so we've developed we actually invested quite a lot in the last year to try to systematize that process and we found different ways of doing that.

Ronnie Sadka
But at this point, our data is able to automatically capture themes as they emerge, even for the first time as they emerge in our data set. So that's one point that I wanted to mention.

Emmanuel Jurczenko
Thank you very much. There is another question, the first one that come up in the chat box. Is there any way to know the time between when the factor is talked about and the time the factor actually takes effect? And a related point is there a correlation between the narratives that mean do you see any correlation?

Ronnie Sadka
Yeah. So I think the first question is really about the narrative map. Here's the thing you don't know yet. On average, there is some momentum. That's what we find, right? Because if you invest in assets, in baskets that are sensitive, positive, sensitive to a narrative, that tends to outperform in intermediate terms. So there seems to be kind of a wave when you think about narratives. However, some narratives are being discussed and then boom, drop. So it's not easy to understand which ones are going to behave this way or that way. What we found, I think early on is, let's say if you take the COVID-19, when you look at the different variants, the more high discussion there was the beginning, the quicker it fell. But I think it becomes very specific to the narrative. So on average, I can tell you that there's continuation, but it's still an open question to understand exactly the path. I think it's a great question. I don't think we're there yet in terms of fully understanding which ones are going to continue to propagate versus those that are going to drop. The second question. What was the second question?

Emmanuel Jurczenko
The correlation between the narratives, right.

Ronnie Sadka
I think that's another place where there's some weakness in the approach because you could have a narrative like COVID, but you can also have a narrative like pandemic, you can have a narrative of recession and a narrative of inflation, and some of the things are going to be correlated. And so it's important to try to orthogonalize them. I think when you do this new approach of word embedding, you can really, from the get go, try to understand which narratives are actually the same narrative. So if people talk about military escalation, but then there's another narrative which is the Russian Ukrainian conflict, we're actually talking about the same thing. So I think it's important to try to understand that actually you're doing the same narrative and that it doesn't show up twice. So there certainly could be a situation where that happens, but we certainly try to apply some techniques to kind of separate them and are stogenized some of the narratives.

Emmanuel Jurczenko
Perhaps the last question. Hi, professor. Do you have global coverage of the stocks? Not just USA or English speaking media? So the point is your geographical.

Ronnie Sadka
Yeah. It's a great question. What I've shown so far is really English speaking, but I want to make sure. Okay, so let me say two things. First is we do collect information across the globe in different languages and forget the number ten or whatever languages for the analysis. We use only the English speaking. But I want to mention we looked at how because we worried about that, how good is our coverage. And I'll give you an example. If you look at the French, let's say "Le Monde Figaro", they also have the English language website. So you can always get for many across the world, we can get also English speaking website of local media. So we actually don't feel in terms of where you want to put in your effort. I think in terms of the coverage, we're actually probably doing fine. We could do better. But I think as a business we rather can okay, say here we're good with the coverage and then let's talk about the application, rather than, again, trying to understand exactly all the different type of language. It could still be that when you write something in French and there's a nuance when you use the translation in English, it's a different context. I appreciate that, but I can say that we do cover we have very good coverage in Asia, in Europe, some in Africa, the Middle East. So we have a pretty good coverage south America, we have a pretty good coverage across the globe.

Gideon Ozik
And just to jump in, last night we presented, for example, a very localized case study of how you measure attention and exposure of firms in Asia to Escalation in the Taiwan China conflict. So that didn't limit us. Another thing we tend to do, it depends on how from the research side, you want to do everything, but from the business perspective, as Ronnie said, you want to make sure you focus your energy and capital on things that would yield return. But having said that, we have I can't remember at least three specific local examples, one on Italy, another one in Mexico, another one in Brazil. So we just made an extra effort to sort of build local reservoirs of information in the local language. We actually hired locally kind of fluent people to help us build the models in each one of these geographies, each one for the very different use case. And then we kind of sort of run with them. So it's not a global approach, but it made sense because the opportunity was there at the.

Emmanuel Jurczenko
Okay, so thank you very much. Thank you, Gideon. Thank you, Thibault. And most importantly, thank you, Ronnie. Thank you all. It was a great session, fascinating. I would like to have the app. I don't know if it's possible to.

Gideon Ozik
See all the we'll give you a discount.

Emmanuel Jurczenko
Good to know. So thanks again, guys. So before we close down, just take the moment to mention that the next session will be on the 13 December. We'll have a Floriant Berg from MIT that will talk about ESG ratings. So looking forward to the next session. And again, great. Thank you. To Ronnie Gideon, Thibault.

Thibault Uhl
Thank you.

Emmanuel Jurczenko
See you. Bye bye. See you in Paris, Ronnie.

Ronnie Sadka
I hope so. Bye bye.

Gideon Ozik
Thank you. Thanks a lot, guys.

Ronnie Sadka
Bye bye.

Emmanuel Jurczenko
Great session.

Ronnie Sadka
Thank you.

Make an impact
EDHEC BUSINESS SCHOOL
[Music]

 

[Music]
EDHEC Business School
EDHEC Speaker Series - The future of Finance
Passion assets

William Goetzmann
Professor of Finance
Director of the International Center for Finance
Yale School of management

Tuesday, 18 October 2022
6 P.M - 7 P.M Paris time

Emmanuel Jurczenko
Okay, so let's go ahead. Hello, everyone, and welcome for the new session of the EDHEC Virtual Speaker series on the future of finance. I'm Emmanuel Jurczenko. I'm the director of Graduate finance program here at EDHEC Business School. It's my pleasure to welcome you today. So I want first to introduce our co-moderators for today's session. So we have my colleague, Professor Laurent Calvet. Laurent, which is one of our main experts on empirical asset pricing at EDHEC. We have also Marianne Chang, that is a student in the MSc in Corporate Finance and Banking. And it's my pleasure to introduce our guest speaker for today's session, so William Goetzmann. So Will is a professor of Finance and Faculty Director of the International Center of Finance at the Yale School of Management. Will is an internationally renowned financial economist, has published scholar articles on so many various topics. Difficult for me to summarize them, but I will just mention Stock Market Predictability, Hedge Fund Performance, Real Estate, Private Equity, Alternative Assets. We'll also have written and co-author a number of reference state books, including, for instance, modern Portfolio Theory and Investment Analysis, Money Change Everything, one of his last book, How Finance Met Civilization.

Emmanuel Jurczenko
And we are very fortunate to have him speak today about NFTs so non-fungible tokens as perhaps the future investment class. So, without further ado, Will, I will welcome you and give you the virtual floor. The virtual floor is yours, Will.

William Goetzmann
Well, thank you very much. It's a pleasure to be here and to be speaking with you. I'm looking forward to the questions and discussion. Let me preface this before I show my slides with a little bit of background. I thought that when I was young, I was going to be an artist. And so my life was focused on the arts. And you can see from my office that it still has somewhat of that focus. But when I made a transition to economics and finance, I wanted to have something to bring with me from my prior interests in life. And so that got me started on a career of exploring art as an asset. So the talk today is going to be about, quite broadly, how art serves and can serve as an asset. And you'll see also why it interests me as an economist. Let me now share my screen. And we use this term "Passion Assets". And you can imagine many different things that people can get passionate about, everything from works of art and beautiful houses to ESG assets and things that are designed to change the world. But we're going to focus on collectibles today.

William Goetzmann
So I like history as well. So I'm going to start back, deep back into time. And these are little stones that archaeologists found in a cave in what's now Israel. It's called Qesem Cave, and it's one of the earliest human habitation sites in the Mediterranean area. And archaeologists began to find these little interesting, colorful pebbles in this cave where the occupants, even they predated Homo sapiens. And why were the pebbles there? They couldn't have gotten washed in. Well, they were collectors items. Very clearly, people that lived in that cave would go pick up interesting stones, bring them in. For what reason we don't know, except that you can see some of them are quite beautiful. So that notion of collecting beautiful things certainly extends back earlier than the history of Homo sapiens. Here's a couple more that are particularly kind of cool. One of them is a pebble that somebody found in South Africa that is about the same age as those little stones I showed you before. It looks like a person, but the other one is a hand axe "Coup de poing", I think it's called, which is a tool. But the tool is more than a tool because it's got a shell right in the middle of it.

William Goetzmann
The ancient person that made that tool must have chosen it because and designed the tool around this idea of a shell in it. So you can see that this notion of making special things or collecting special things is quite embedded in the human psyche. It must be as important in some sense or almost as important as our economic incentives. So now I want to move on to what an economist will think about art, and you'll see why I'm sort of interested in this area of research as well, because I like to think about collectibles in general as non fungible assets. We call the new electronic non fungible assets NFTs. But the difference between fungibility and non fungibility is quite interesting when you start to think about valuing these things, what's the price of something like one of those beautifully made tools or something? So it's the difference in economic terms between assets that have a common value and are substitutable for each other and private value assets where the specialness of the item in some sense may be more valuable to one person than the other. This issue arises when you think about putting a price on a house, for example, whether one person likes to live in a house with a beautiful view or another person likes to live in a house that's close to a park.

William Goetzmann
Those are things that people have different views about. But even more than that, the notion of a unique asset, a non fungible asset that has no perfect substitute, may actually be an endogenous feature of this market. People like to pick things that are special to them and represent their special taste. So the person that picked out one of those beautiful red little pebbles might have chosen it because it was different than all the other pebbles. How do we think about that as economists? And then how do we actually build some measure of the value as an asset? This has interested me for probably about 30 years and has provoked my research constantly to investigate. So some of my early research was really about how do you build an index of art over time so that we could compare it to stocks and bonds and other kinds of assets. And here's a picture of a work that I did with Christophe Spaenjers and Luc Renneboog, which is an index of the capital appreciation of a portfolio of art from 1765 up to 2005. I haven't extended it further because it's harder and harder to get this data.

William Goetzmann
But when you look at it, you can see a few interesting things. The most important for an investor is this index goes up on average, and when you correct for inflation, it still goes up more than inflation. The other thing to look at is what happens around these periods that we know to be difficult times in history. The First World War, the art went down a lot and the crash of 1929, the art went down in the United States and in Britain certainly too. 1973 was a terrible year for the stock market. Art went down there, and then even in 1990, there was a bubble in tech stocks that burst. You can see art may be something that doesn't generate dividends. It may be something very different than productive assets in the economy. But for some interesting reason, it seems to actually move very broadly with measures of other asset values. So the natural thing, if you're a financial economist, let's look at the beta of art. And so I put in a measure of the UK stock index, just the index without the dividends, just to give you a picture of how art and stocks actually had a positive correlation over the period 1830 to 2005.

William Goetzmann
And the beta that we have estimated from this relationship is 0.4. So if somebody says, what is the expected return to art? If it was efficiently priced, you wouldn't expect it to match the stock market. You'd expect it to have a somewhat less return. When we compare art, the return of art, at least without transactions costs and so forth, to the return of stocks plus dividends, you can see that the rate of return is significantly lower than investing in the stock market, at least over the long term. So that's another lesson that we were able to learn when we looked at the historical rates of return.

William Goetzmann
Another thing that's kind of interesting is, and this may be because art has some role in society beyond its value as an asset, of course it may be a signifier of wealth. It could be something that distinguishes somebody as a great connoisseur. But with Christophe Spaenjers and Luc Renneboog, we tested whether there was a relationship between income inequality and those art returns. The performance of art that you saw in the previous picture and we took the income inequality measures from Thomas Piketty and we were able to identify a positive relationship between the change in inequality and the return to investing in art.

William Goetzmann
So whenever you see a box with some numbers and three stars, you can tell an economist is excited about a result that is statistically significant. Part of the research into art that has kept me fascinated is what are the broader social roles that art plays and why are people willing to spend money on that dimension as well? Okay, I promised a little bit of further information about valuation. I think what I'd like to show you is the complexity involved in trying to value a single work of art and a little bit of a sense of how we estimate risk and return. Here's a painting by Joan Mitchell, a wonderful abstract expressionist painter. She actually did a lot of her work in the south of France and her work currently sells for a lot of money. And here is a picture that was painted in 1989 and it came up for auction. And the problem the auctioneers have is how do you put a price on this thing? Well, I've done some work on that as well, both in practice and also in academic research. And I will tell you the most important determinant of the value is the identity of the artist.

William Goetzmann
So knowing it's a Joan Mitchell painting tells you a lot about whether it's a million dollar painting or a hundred dollar painting. And you know, you might not be able to tell that by looking at the art. You know, the importance of the work in the artist's own earth and of course, whether or not it is actually painted by the artist. Those are things that are essential to the valuation. But when it comes down to it, since this is a unique work of art, we have to use something like comparable sales, but there's no perfect comparison. So what do we do? We actually use a linear regression for this and it's a regression that is called a hedonic regression because we can represent the characteristics of this work of art by X variables and it's quite general framework for doing this estimation. But what we need are past sales of Joan Mitchell's art. That's the data that we have to run the regression. So if you're ever thinking about doing this, there are databases, and I've told you down at the bottom where you can access these data, there are databases where you can get all of the past auction sales and you can even get pictures of the works of art.

William Goetzmann
And then you can see all of these have potential to be the hedonic characteristics that you would put into a regression. For example, if you look at the year, some of the you may not be able to see it well enough, but there's some of the paintings that were painted in the 1950s that were earlier in her work and then some later. So the 1989, that was a bit late for Joan Mitchell. Then the other variables are the width of the painting and the height of the painting. Of course, we also have the date that the painting sold, so when was it auctioned? And then we also have these estimates that the auction house itself placed on these paintings. And then finally, we have the actual price that the painting sold for. So we're going to try and explain the actual price of those paintings by these characteristics. And then we can use the characteristics of the Joan Mitchell painting that we're interested in to estimate the value. Once we build a model with our regression, we just put in the characteristics that we're interested in estimating.

William Goetzmann
So, the next slide is the result of that regression. So, first of all, do we have a good model? And what is the model? Well, I just chop down the model, the variables down to the width of the painting and the height of the painting, and what the number of years in the past that the painting was painted. And then I put a one in each one of a series of columns for each one of the years that the auctions were observed. So that's what these are called year dummies. And that's because sometimes the whole art market is up in that year or it's down. So you can see some of these year dummies, like 2016, there was a big positive coefficient on this year, meaning it's probably a good year for art. Then we have our t Statistic to tell us whether or not these things are significant. Now, every single time I've ever run a hedonic regression with art, it has told me that the size matters. So once you know who the artist is, then you need know how big this is. And you probably also need to know whether it's an oil or not.

William Goetzmann
So you can see the wider the painting, the more valuable. And then the earlier the painting is in her work, the more valuable it is, too. So that's how we go through and try and estimate the value of a work of art. Now, look at this. The standard error is huge. So the model predicts that this painting could sell for between 5.8 million and 1.6 million. That's a very big range. So, if you are investing in art, you trust the statistical analysis. Take this uncertainty seriously. It's big risk about what you can sell your resale your art for. The actual painting, by the way, sold for $3.75 million. And the estimate by the auction house was not as broad as our estimate statistically.

William Goetzmann
Okay, now I want to talk about my current research. This is a picture from Tulip Mania from the 17th century in the Netherlands when people were spending an enormous amount of money buying crazy things, Tulip bulbs. And it's the most famous bubble in all of financial history. Well, we all have had the opportunity to live through one of the most extraordinary bubbles in assets in centuries. Which is NFTs, non fungible tokens, you can judge actually when I show you the evidence about whether it's an extraordinary bubble, but it's very similar to the Tulip bubble because it's not really a main financial asset, but it's a work of art. It's a special non fungible thing that suddenly shot up in value.

William Goetzmann
I'll show you some NFTs just to give you a sense of what they look like. I showed you the pebbles before. Now I'm showing you these NFTs that are the modern versions that people collect these little items. One of them is actually an AI generated portrait, which is kind of cool, but think how much money you would pay for one of those. Now look at the price that somebody else paid for. One $3.3 thousand, 0.27 thousand, here's 5.2 thousand... So people are paying thousands of dollars for these things. And now these are the most famous, they're called crypto punks. And now look at the millions of dollars that these things were selling for in 2021. $7.58 million for that little thing. And it's not physical, by the way. It's just an electronic file that anybody can I've got it right here. I can look at it anytime I want. So, with some co-authors, Dong Huang and Milad Nozari, we have been investigating this bubble. And so I can show you, for example, in dollar terms, which is the black line, the price in dollars.

William Goetzmann
If you bought something for $1,000, you'd say, well, the index, it looks like $1,000 investment may have gone up to $5 million. Okay? That's the kind of excitement that people had about this. From 2018, it was kind of flat and then all of a sudden in 2020, people started to get interested in these NFTs. The price started going up, and then in 2021 it was spiking. And then it looked like it still was going up towards the early 2022. That's what these simple indexes of the median values seem to be showing. Okay? Actually, those are the max values. The median values are a little bit more modest, but they do show you that they suggest that these things were high towards the end. But we really had our doubts about this.

William Goetzmann
The doubts are based on the hope that people had that what they bought in 2018 was really going to keep going up. So these are the asking prices, actually, the seller reserve that we look at asking prices for what people, once they bought an NFT, what do they try and sell it for? And what you see is that on average, they want to sell it for six times the price that they bought it.

William Goetzmann
So imagine buying a house and then putting a price tag on the house that's six times higher. It seems crazy, but sometimes people were getting those extreme prices. That is the thing that can really generate a bubble, this kind of expectations that seem to be fulfilled. And people just keep buying even, they just keep on asking and bidding these things up. So what we noticed, however, is that towards the end of this time period, these NFTs were not selling. Liquidity is really an important issue here. And so look at the green line versus the black line. Now, I'm not going to go into the complexities of how we did this, but I will tell you the general econometric issue that we got interested in is. How do you correct for the fact? That even though there are a few sales that are high enough to exceed that six times original price. If you're only using those to create your index, you're almost certainly overestimating the value of all the things that didn't sell in that same time period. How do you correct for that? Well, we have models in econometrics to correct for that kind of selection bias.

William Goetzmann
And with advanced techniques of optimization and kind of a Bayesian approach, we're able to construct a selection-corrected index. So what you can see here is the selection corrected index in the green follows a different path than the uncorrected index, which we're calling a repeat sale index. And you notice that the uncorrected index grows by a very high number, sort of 5000 from roughly 100 to 5000. But the green index, once you correct for that, grows by much less. And in fact, what you see is there's a big crash in early 2021 that was not observed when you looked at just the things that sold only. Once you take into account the fact that things didn't sell, there's a big difference. If you're an investor, what does this mean? Well, first of all, it's not going to help me to tell you that when liquidity dries up, try and sell your assets because you can't do it. But it's important to know that if you're investing in a non-fungible asset, those markets can dry up. You have to consider that risk. So now I'm going to show you a bit more about what if we simulated an investment strategy based upon the NFTs that sold over that time period, 2018 to the present, or at least till early 2022.

William Goetzmann
We can do that because there's fantastic data about NFTs, so we can see a purchase and a sale price. And so what we did is we asked ourselves, what if we were going to, every month we were going to buy NFTs, hypothetically buy the NFTs by the most liquid artists, liquid creators of NFTs, and then hold them, and then we will realize the sale when those things sold again. Well, first of all, most of those NFTs, most of those artists didn't really generate any profits. The only profits really were coming from the last 10% of the artists. And really the high profits were just one or two artists. So it's very much a superstar market. Just like a venture market, venture capital. You'd have to invest in a lot of these things in order to be sure you're going to make some money.

William Goetzmann
So next we're going to look at liquidity. And let me just show you this one picture, which is a measure of the probability of resale. In other words, let's say that you bought an NFT in our sample and then you want to know what was the time until it was resold. Well, that's what this chart is. It's a survival curve that people use for medical purposes to see what's the expected lifespan people. And the half-life is at the 50% market this says 50% of these NFTs were resold within about 120 days. So it's not like they were able to be resold quickly. And it takes a long time for you to expect that you could resell. As a matter of fact, you still are stuck with about 30% of your NFTs after two or three years of holding on to them. So, like in private equity, you're not always able to sell the companies that your private equity firm has bought in a timely fashion. And finally, this is the most interesting one out of about 4000, actually more than 4000 repeated sales where we can measure the returns. We started dropping out the highest returning transactions and we found that after we dropped out just a little more than the top 200 transactions out of all the 4000 some odd transactions, after 200, your internal rate of return, which is on this axis, goes down to zero. So again, just a handful, only 5% or so of these NFT transactions were driving the whole level of profits.

William Goetzmann
I'll leave you with a little bit more on the institutional side. Up until recently, if you're interested in NFTs, you would see that people were suddenly creating investment portfolios from them, using them to back new products. And then a whole new marketplace has also evolved for you to buy regular works of art. I would say what I've just showed you about the liquidity and about the bias in what the historical rates of return might be should give you some caution about jumping into this interesting, volatile and unusual kind of markets of NFTs and for that matter, also into the art market. So I will stop here. Just wanted to give you a very quick overview of things that I've worked on in the past and things that are exciting to me now about nonfungible assets.

Emmanuel Jurczenko
Okay, thank you very much, Will. Thanks for this great presentation. And I will say hot topic. So now I'm passing to Laurent. So, Laurent, if you want to say few words or ask any questions about the topic and the presentation of Will.

Laurent Calvet
Yes, well, thank you, Emmanuel, for giving me the opportunity to catch up with Will, really, after so many years. So, just before the webinar started, we were reminiscing that the first time we met was over a paper that I had co-authored with Benoit Mandelbrot and Adli Fisher and was a paper about the application of multi-fractals to finance and in some know, fractals. I think the success of fractals come from the fact that they are beautiful, right. They are ways of generating very nice works of art. So now I see that we've transitioned from fractals to NFTs. My question really is about quality, the quality of art. So clearly, when you look at old paintings, there's no feedback, I would say, between the innovations that we see in the art market and the quality of the work that Van Gogh or others have done. But in the old days, like Van Gogh was lived poor, died poor, and now we have all these financial activities or this financialization of art. So I was wondering if there was a feedback loop and this in some way affected the quality of the art that's produced.

William Goetzmann
Yes, that's an important question and also a question about the role of the art in society as opposed to simply as an asset. But you're asking also about the feedback of the market back into the production of the art. There was a sale in early 2021 by an artist who's known as Beeple for $69 million at auction, which it's an electronic work of art, and Mike Winklemann is the man's real name and that transformed the marketplace and expectations about the value. But also, Mike Winkelmann's art is, I would say, surrealistic, contemporary, techno surrealism, where he will create fantastic creatures in a peculiar science fiction landscape. His art, that kind of characterizes a lot of NFT art because Winklemann was a computer scientist before he became an artist, or actually he's always been both, you know, that's a particular subculture in our society. And his art appealed to that group first before appealing to the fine art. This auction was important because it was clearly an attempt by the major auction houses, Sotheby's and Christie's and so on, to get their customers involved in this new art form. And for them to get involved in the new art form, they had to develop a whole new set of tastes, which probably isn't unusual because every weird new contemporary work of art collector had to already adjust their mind around these things.

William Goetzmann
So this art world at the very high end is adjusting. Meanwhile, artists that were making things, like Mike Winklemann said, well, this is great. There's a chance for me to become a multimillionaire. So he's showed us what sells, so let's move in that direction. That, you know, this is just what it was. Just a year or two over the last two years, it's been people trying to figure out, will there really be a high end collectible market? How do I redefine myself so that Winklemann's customers will buy my art? And then, amidst all of this, artists who really never have had a chance to get top galleries, they see this as a new medium for them to be able to reach directly to an audience. And also you saw those motion things. It's a new medium for them to explore their expression as well. So we don't have any idea where it's all going to come down. But the problem with any kind of collectibles market is if there are too many people that are speculating and not appreciating the works of art, then when they dumped all their NFTs, what's the final set of collectors that really love these things?

William Goetzmann
And does it expand beyond the kind of techno, computer-oriented clientele that it started with? And we don't know that yet?

Laurent Calvet
Yeah, exactly. I have another question. I mean, you showed that in NFT markets, very few works of art make it above a threshold. I think it was $25,000. In the more traditional art market, you know, it's very common to pump up prices. So you know, you have a ring of dealers and they identify an artist. They decide to pump up the price by buying and selling the paintings in different auction houses, and then collectors notice that the values goes up. Is there room for pumping up prices in these new NFT markets?

William Goetzmann
Oh, absolutely. That's been one of the concerns even in my I'm sure in my data set, there must be some examples of people secretly bidding their own works of art up to high values. So the securities and Exchange Commission is interested in this. The regulators are interested in whether or not there's price manipulation. Once you're talking about millions of dollars, they're going to be very attentive to whether or not there's price manipulation. And so that's a concern not only for people trying to estimate for us trying to estimate returns, but also that's a risk that you face when you're playing in this market, and you don't know whether your counterparties are honest. So that's a big issue.

Laurent Calvet
Yes, okay. That's what I thought as well. But thank you for confirming it. Maybe I have one last question before Marianne takes over. So you mentioned that there is a link between the returns on art and income inequality, and I noticed in particular that it's income inequality. It's not the share of top income that shows up in your regression. Can you comment maybe a little bit more on what you think as the underlying mechanisms creating these correlations?

William Goetzmann
Yes, it's not in that paper. We talk about it a little bit, but the mechanism that we have in mind might be like a Thorstein Veblen effect, conspicuous consumption. I did some work with Sharon Oster, who I'm sure you remember Sharon from the economics group at the School of Management when you were here as a doctoral student. But she and I worked on the role of museums in cities, and we found that in cities where there was a sudden "Nouveau riche", that museums were supported by a lot of donations suggesting that the museum had a social function to create a pecking order. A lexicographic ordering of people in the city. So that's one hypothesis that we had that in terms when people are making a lot of money, there needs to be another signal besides simply wealth to differentiate somebody socially. So that was one idea at the back of our minds when we were running those tests about inequality, but it's hard to think of better tests about how to actually do that. By the way, you reminded me, of course, Benoit Mandelbrot was a pioneer in electronic art because his fractals, he was showing us these pictures of mountains and clouds and landscapes, but they were all pixels.

William Goetzmann
So he was very early ahead of his time. I wish we could have told him about NFTs, but a lot of them, I'm sure, use fractals, actually, to make them look more realistic.

Laurent Calvet
Thank you very much for your answers.

William Goetzmann
And great to see you.

Laurent Calvet
Yes. Marianne, I think, has questions for you.

Emmanuel Jurczenko
Thank you, Laurent. So, Marianne.

Marianne Chang
Thank you again for this presentation. It was very interesting to learn more about patient assets and techniques used to value them. So you mentioned during your presentation the importance of artist identity when it comes to value on art. And I read that in 2018, Christie solved the first art made by artificial intelligence, and in this case, there were no human artists behind the painting, but only on the Argolism. And my question was, do you think that in the wake of AI artwork, the importance of artist reputation in the valuation of the art is going to question?

William Goetzmann
Well, that's a really interesting issue. It's been a few years since the very first AI work of art was sold. And if I remember correctly, it sold for quite a bit of money at an auction. It could be that was the case of the auction house trying to build out a new try to instigate a new market. But I think it's going to depend on how beautiful and interesting these things are. They're getting more interesting visually every day. Just over the last few months, as we all begin to play with these tools, we find they make things that provoke your imagination. So I don't know what the value of them will be, because we don't know whether how unique each one of the images will truly be. If there's an artificial artist, how similar will the artwork be? Maybe we should treat the artificial artist as something or somebody that has to develop a reputation and an IRF to react to before we can evaluate. But, you know, the artificial artist is ultimately a programmer. So if I were the programmer, I would be taking credit for creating this automaton.

Marianne Chang
Thank you. And I have a second question. So now, in the wake of NFT, I'd like to know if the profile of the buyer of the buyer of those NFT are the same of those who buy physical painting, or is there some difference?

William Goetzmann
We don't know that yet. We do know that there is kind of a jet set of art buyers that immediately felt compelled to invest in the latest thing. So there probably is some overlap in collectors. But I think in terms of the clientele that loves the work of art and has followed these artists for a long time, I think that it's fairly separated so far. And whether or not the tastes will develop or even the social value of these NFTs will fill the same role is open to question. Collecting NFTs is a little bit like collecting picture books. You have to open up something and look at it. It's a personal experience. It's very hard to easily display them, although Samsung has a new television that's designed to allow you to do that. But we don't know how you can show off these things. It's consumption. But will they be conspicuous consumption and how? We don't understand that.

Marianne Chang
Thank you. And maybe one last question. So you've done a lot of analysis on art valuation and between physical painting and NFT from your point of view, which is the best investment?

William Goetzmann
Well, I think I showed you that evidence that NFTs are extremely risky investments. Are they riskier than paintings? We know more about the long term history of paintings. But I tell you, if you go back to the 19th century and you say, I'm an art collector in the 19th century, what would I be buying? You probably would have been buying things like furniture and rugs. And so it could even be from our perspective that paintings turned out to be the medium for the whole revolution and abstraction and so forth. But maybe even paintings are just a temporary phenomena in the history of art. So I would still bet that paintings would be a long term investment that will hold value compared to NFTs. But then again, you would like to believe that it's worth it to invest at least a little bit in the beginning of a new art form. If you could have spotted a Van Gogh in his brother's window when he was selling them and just bought a small one, that would have been a wise thing to do. So I keep my eye on those prices of those crypto punks, because if they ever completely collapse, I'm going to buy one because those are the icons of this bubble.

Marianne Chang
Thank you for the answer.

William Goetzmann
Thanks for the question.

Emmanuel Jurczenko
Thank you, Marianne. So there were a couple of questions in the Q&A one which is related to some for Marianne. My question is the following what are your thoughts regarding NFTs representing and accompanying real art pieces like paintings or watches. That are the proposed at auctions like they did with NFT drawings of Gerald Genta watches?

William Goetzmann
Well, right now, even the auction market for NFTs is a bit fragmented. The major auction houses felt left out because the data we use is from an NFT auction house called super rare. So there's this competition about where the ultimate venue for these will be. And so the question being, will they accompany real art pieces or even collectibles like watches? I think that the auction houses are really trying hard to get them to be accepted as a category of collectible and they're trying hard to establish themselves as the tastemakers for what is the very top that you could buy, because the market is undifferentiated right now. Even though super rare, for example, is a curated marketplace, it's not curated to only take things that sell for the 10 thousand, 100 thousand and so forth. So there will probably be, particularly if these NFTs have a social value, there will probably be a marketplace that is for the super rich. And so whose marketplace will that be? Will it be Christie's and Sotheby's and some of the other auction houses? We don't know yet! But I'm sure that's an issue for the strategists in these auction houses.

Emmanuel Jurczenko
Thank you. From my side, I have a question with respect to we're talking about tokenization of art, right? What are your views with respect to tokenization of other alternative assets like private equity, real estate? So what do you think about this movement which basically tried to digitalize several alternative form of investments?

William Goetzmann
That Masterworks Stat IO website is trying to create a marketplace for fractional shares of works of art. And so you can imagine all sorts of challenges with that. Like what's the valuation at any given time? But I think that that's a beginning of the creation of liquidity in works of art. Once you have some works of art that are worth millions of dollars, then of course securitization might make some sense. I think we're going to move in that direction further. There will always be questions about the mechanisms, the valuations and so forth, but those are all just risk issues. The one benefit, of course, is to tokenization, or at least some form of fractional ownership, is that the artists themselves can continue to participate in future sales. And that is what motivated it, particularly in Europe, but it's catching hold in other countries as well I think.

Emmanuel Jurczenko
There were another question. Do you believe NFT's impact on the environment could lead to their downfall? Also, do you believe that NFTs can be sustainable? To what extent?

William Goetzmann
That's one of the most important questions. Because for those of us in the world of finance and investments, the first question we get every day, waking up in the morning is what about ESG? And so how can you have an investment like an NFT if the electricity costs for the verification of the transactions every day is sort of equal to the electrical generation, the cost of electricity generated in a country like Belgium? I mean, I'm exaggerating because I don't know the actual numbers, but that has been a big issue. How can somebody believe in NFTs if the transactions generate so much carbon? So all the NFTs that I showed you in the presentation traded in Ethereum. So Ethereum is well, just within the last month, they transitioned to a new form of verification that they claim has cut down the carbon footprint by a factor of 100. I think something like that. And what that meant to me is suddenly you can feel a lot better about investing in something that uses the blockchain if it's the Ethereum blockchain. Now, whether or not they're correct in their description of the carbon footprint and so forth.

William Goetzmann
I'll leave it to other people that study this, but I think all of the verification processes are going to have to move to a much smaller carbon footprint mechanism. And Ethereum was wise to do that now, and I think that will maintain its lead position in the NFT market.

Emmanuel Jurczenko
Okay, so I don't see any other questions. So I don't know if Marianne or Laurent want to ask a last question. If not, I just want to thanks to all our speakers.

Emmanuel Jurczenko
So. Thank you, Laurent. Thank you, Marianne. And most importantly, thank you very much, Will, for this presentation of this new form of art investing. This was super interesting and we look forward to read the final version of your research paper because to my understanding, you are the start of the research process on that. So thanks.

Emmanuel Jurczenko
Before we close down, I just want to mention that we'll have a next session that will be on 15 November, same day, same time, and we will continue our Journey on the Future of Finance with Ronnie Sadka from Boston College that will talk about some application on big data investment application using media stats. So thank everybody. Thank you and see you soon. Thank you, Will. Great time with you and hope to see you in Nice or in Switzerland for Private Markets Conference. See you will. Thanks to all. Thank you, Laurent. Thank you, Marianne.

Laurent Calvet
Thank you very much

Marianne Chang
Thank you

William Goetzmann
Thank you. I really enjoyed it, appreciate your great questions and like spending time with you virtually here. So, again, I also hope to see you in Nice.

Laurent Calvet
Merci beaucoup Will à bientôt

Make an impact
EDHEC BUSINESS SCHOOL
[Music]

 

[Music]
EDHEC Business School
EDHEC Speaker Series - The future of Finance
Climate Finance: From climate risk to financial risk

Stefano Battiston
Professor of Finance at the University of Zurich,
Professor of Economics at University of Venice

Tuesday, 5 May 2022
6 P.M - 7 P.M Paris time

Emmanuel Jurczenkoa
Okay, so let's go ahead. Hello everyone. Welcome to our new EDHEC Speaker Series on the future of finance. So, I'm Emmanuel Jurczenko. I'm the director of the Graduate Finance Program here at EDHEC and it's my pleasure to welcome you today. So let's move to our program. I'd like first to introduce our co-moderators. So today we get professor Riccardo Rebonato, which is one of our experts in Climate Financial Economics, Climate Change Economics. He's also the academic Director of our new Research Center RC, which is specialized on Climate Change, Climate Finance. We got also Yann Lancernon, that is a student in our new MSc in Climate Change and Sustainable Finance, which is a joint program in partnership with Ecole des Mines in and Crédit Agricole for corporate partner. It's now my pleasure to introduce our guest speaker for today's session, so Professor Stefano Battiston. So Stefano is actually professor of Banking Finance at University of Zurich. He's also professor of Economics at the University of Ca'Foscari of Venice. He's a renowned Financial Economist or Economist in the field of Sustainable and Climate Finance. He got several position, he get says it's a Deputy Director of the Finances Center for Financial Networks and Sustainability at University of Zurich.

He's a member of the Group of Economic Advisors of the European Securities and Market Authorities, also called ESMA. He's consultant for various in fact, governmental supranational institution, the European Commission, the ECB and so forth. And last but not least, he's also the lead author of the 6th Assessment Report of IPCC chapter on Finance and Investment. So we are very fortunate to have him speak today about Climate Financial Risk associated to the transition toward a zero carbon economy. So, without further ado, let me turning to Stefano. Stefano, welcome. And the virtual floor is yours.

Stefano Battiston
Thank you very much for the very kind introduction and for this invitation. I now will be sharing my screen. Could you please confirm that you can see my slides?

Emmanuel
We can see them

Stefano
All right. Excellent. So it's my pleasure to speak today about Climate scenarios, transition risk and Climate stress-tests. Here's the outline of what I would like to talk to you about. I will start with some messages that I believe very important from the IPCC AR6 report, fixed Assessment Report. The IPCC has publishes reports in cycles of about seven years. So it's an important year, this one. Then I will move to some concepts about climate economic scenarios and how to use them for climate stress-tests. I've prepared as but they are in an appendix if time allows and if there are questions on that about the missing endogeneity of climate scenarios and why it's important that it's missing and why it's important to restore it back in our models. Let me say, maybe as a preamble, this is a very broad field by now and it's very difficult to give a concise sense of it in just one lecture. In fact, I've been involved in a number of series of lectures actually with Irene Monasterolo. We gave a series of lectures at Banca d'Italia two months ago. So there is a lot of interest by financial advisors. So here is just a selection of some of the directions.

So you see chemistry testing more specifically for transition risk, but also physical risk. Here you see some papers. To some extent we had the luck to open up somehow this area of research with a paper on the climate success in the financial system back in 2017, which was the first to connect the integrated assessment models from the IPCC to financial risk measures. And then you see what you see on the right hand side papers about scenarios, how they are sensitive to assumptions, how these impact on the risk measures and then these aspects of endogenity. And then there's a number of policy applications. So these are, if you're interested in papers that are kind of more qualitative and looking at the policy implications here, they listed that there for your convenience. So let me start from one important question. How much money would be needed to invest to be invested in order to achieve the Paris agreement and how much money is actually invested? So before we look into that, and I'm now here extracting from some of the chapters of the 6th Assessment Report of the IPCC. The first thing we need to realize is that the mitigation of climate change implies a rather large and deep transformation across many sectors.

So from what you see in this slide on the left-hand side is how much the fossil sector as a whole would have to shrink according to different scenarios that have been developed and reviewed within the IPCC from to 2030 to 2050, for instance. And essentially the message is across all decarbonization scenarios there is a substantial decrease of output in the fossil fuel sector as a whole in the range of 50 to 60 or even more percent. So that means the entire sector has to shrink if we want to achieve those targets. And another example is shown on the right hand side where this instead is the share of the generation of electricity that would have to come from low carbon sources. Again if we want to achieve decarbonization targets. Don't get confused by all these box plots. This is the typical representation of many of the charts you find in the IPCC report. So every box plot here is representing a different scenario. There is a family of scenarios, so we have in particular. So here's the message is that depending on the severity of the scenario in terms of the level of decarbonization and the target you want to reach, there is an increasing share.

So what you see is that these scenarios that the blue scenarios on top here imply that going from 2030 to 2050, and even more so would be if we compare from 2020 to 2050. The share of electricity from low carbon energy would have to be essentially go about almost around 90%, starting from something like about 40% and even less that it is today, on average in Europe. Now in the European Union we are about 30% on average. I'm not talking about France. Which means on the one hand the fossil sector in the carbon scenario will have to shrink dramatically. On the other hand, the electricity sector has to change dramatically in terms of the technology that is used. So that's point 1. Point 2, that may require a lot of money. How much money? And this has been reviewed in chapters 15, which is the chapter in which I have contributed. And one of the estimates is essentially that the needs exceed the current flows by a factor 3 to 6 and there are variations across sectors, across regions on this number. That's a big gap to be filled. And then the next question is obviously is this fresh new investments or could be these achieved by reallocation of capital?

And the good news is that most of it is capital reallocation. And we can see that in an example on the left hand side. This is the annual average investment. By the investment here we're talking about capital expenditures. So these estimates do not include the financing cost, for instance the cost of debt. It's just capital expenditures. And you see that in 2019 the annual total investments in the global combination of fuel supply and the power sector was about 1.6 trillions and in 2030 would have to be about 1.9. So it's not 3 to 6 times larger, is about 20% larger. So what this implies is that essentially a large part of the capital is already available. What is needed is a reallocation. And then next question is okay, but why it didn't happen so far? The science has been clear for many for now 2, almost 3 decades. Staying on business as usual path would lead us to very high physical risk. The money is there. Why is it not flowing into those economic activities that would allows us to mitigate Climate change? And this is why we need to investigate now the relationship between Climate and Finance.

This is a bit complex relationship when you actually start digging into the details. And so I'm offering now these diagrams which is not directly included in the chapter 15 from the assessment report is my conceptual illustration of the content that is there. So there are two sides of this relationship. On the one hand, Climate change impacts on Finance and it does so through different channels. There is the physical risk and there is the transitional risk. What is the physical risk? Well first of all it means that directly there is a direct impact on the increasing frequency of magnitude of hazards but also chronic impacts. So we're talking about hurricanes and storms, but also chronic impacts such as droughts and sea level rise. Of course, which impact losses on physical assets say you have a production plant that is going to be flooded more and more frequently can also be losses in terms of human lives in particular if there is insurance associated with that there are indirect effects of the physical risk and this can be quite severe. So we're talking about for instance the fact that reduced food and water security increase the risk of conflict.

That can decrease very much the value of both land and businesses in areas that are affected by these conflicts and this is for physical risk. Then there is also transition risk. Now why there is transition risk? Well in principle you could do, we could do as a whole, as a society we could engage in a transition to a low carbon economy in an orderly manner. What does it mean in a way that is predictable, on a predictable path whereby market participants and in particular financial investors are able to make reliable forecasts on the cash flows of the firms and therefore on cash flows of the securities associated to the firms, say bonds for instance, or equity. However, there are many reasons why the transition could actually be disorderly. What does that mean? Well think of a process, recent process, recent policy process such as Brexit. Brexit for Brexit the date of the policy introduction was known, many of the constraints were known and yet the details of the implementation and in particular the impact that I would has been the subject of uncertainty until the very, very end. What is the reason? Well, the complexity of the policy process as well as the tension between competing interest associated with the introduction of the policy the same unfortunately could happen with the transition to a low carbon economy.

We have on the one hand the incumbent industry, fossil fuel industry on the one hand we have the low carbon energy industry. Then we have also finance which have interest in both sides potentially. Then you have policymakers. Then there is the population which may be in principle in support because it's very much very many people in developed countries, also in developing countries are worried about the consequences of climate change. At the same time when governments are proposing to introduce carbon tax then this has been failed. In Switzerland for instance, the referendum has been rejected. So this complexity leads to the fact that the transition could be disorderly. And if that is disorderly it means that essentially market participants may not be able to anticipate or to hedge against changes in asset prices in particular because the portion of asset is so large this could lead to even financial instability. So now the next question is first of all what is the other direction? There's a direction from finance to Climate change and that has to do with the fact that depending on where the money is invested then there can be an Impact on decreasing climate change or increasing climate change.

Why is that so? Well, it's very simple because firms make investments in particular CapEx, Capital Expenditures into higher low carbon equipment and their decision is influenced by financial actors. Why? Because essentially because the cost of that if financial actors can provide capital to non-financial firms in a way that is more or less expensive depending on technology that is used because of the perception of risk that they have, they have an influence on how the capital may be allocated. So why is this not happening? Why is the capital not being reallocated from High carbon to Low carbon? And in the chapter we've been reviewing number of barriers but essentially most of the barriers relate to the uncertainty on decarbonization plans. What does it mean? It means that there is a lack of clarity on what is the future path of a country in terms of energy consumption, investments in new technologies, jobs, education. You see, shrinking a fossil sector and expanding another sector means for instance, that many people could lose their jobs. So unless there is a clear path on how to handle that and how to accompany that labor that is going to migrate from one sector to the other, this creates a lot of pushing back from various groups in the society.

So that's why the policy credibility has been identified as essentially one of the main element that could reduce this uncertainty. And to conclude on this part, so policy credibility by providing clarity on the decarbonization policies can induce financiers to choose companies that have, how to, say engaged in an investment plan. They have clear investment plan into, for instance, low carbon technology and that is the way in which finance can support overcoming barriers and accelerating the mitigation. I have a number of policy implications here but just for the sake of time maybe I'll skip that. But just to say that for instance, it's not just a single policy will not work. You have to consider cross-cutting policy packages but this is more for economic policies and if you're interested I'm happy to unpack that part. Are there any questions so far?

Emmanuel
No question from the audience.

Stefano
But let me go on.

Emmanuel
The audience can send any question using the Q&A chat box.

Stefano
Okay? Yeah, sure. If there is any clarification question I'm very happy to take it on the way that may be helping other people to follow better. All right. So let's focus a little bit on transition risk. What do we call transition risk? And notice that over time most financial supervisors in developed countries have come very concerned with climate transition risk. And nowadays any single financial, large financial institutions in their annual report they are talking about how they're going to deal with transition risk. Not only that, but by the end of, I believe it's about July this year, a number of banks in the euro area will have to publish the results of their climate stress tests which was conducted upon request of the European Central Bank and this was announced already two years ago. So you see, this is already quite advanced in terms of process. But what is this about? Well, transition risk, the origin of that is what I said before. Agents may not be able to fully anticipate what is the impact of the transition and the announcement of the policy on the values of large portion of assets. Here I'm unpacking a little bit what I said before.

There is uncertainty in the policy process. There are also the social dynamics of market players. Everybody's trying to guess when the others will switch to low carbon investment. And this dynamic is typically leading to something that is going to slip stick dynamics where things will happen suddenly. There is a remark to make on this because a standard assumption in finance is typically that financial markets are good at processing information and that in this case, market players are relatively good at anticipating price changes. But the kind of changes we're talking about are of the type of those if you think about the following events, how good were observers back in a few years ago to anticipate that? What would have been the outcome of the US election in the previous administration? It was unclear until the very end. It was actually sorry, it came as a surprise, the election of Trump, and then in this, the last election was unclear until the very end. Brexit, I already made that example. So these are situations in which it's clear that market players are not able to fully anticipate price changes.

Emmanuel
I think that Riccardo wants to ask me a question.

Riccardo Rebonato
I think you're asking too much of markets because markets can never say that they can predict the outcomes of an election. But they say the moment some information has arrived, they incorporate very quickly. You made the example of Brexit on the day of Brexit I was looking at the screens and it was a big surprise. Pound dropped at the beginning. Sorry, the FTSE 100 dropped at the beginning. Then they realized that the FTSE 100 companies mainly had revenues from abroad, dollar denominated, and therefore they recovered. And the time it took was three and a half hours. So there was a differentiation between FTSE 100 and FTSE 250 in three and a half hours. So markets cannot predict what is going to happen. But much as I don't like them very much, but I still have to be convinced that they are not efficient at processing the available information. Nobody has said that markets can predict.

Stefano
Sure, but the point is that currently so there are different scenarios in possible scenarios in the future. And that's exactly what I'm going to be talking about next. And depending on which scenario is materializing, you may have different levels of output and therefore different levels of cumulative cash flows of firms. So there are different scenarios of profitability of firms. One source of risk is the coordination of agents and coordination of agents on expectations on one scenario versus another one. So the moment in which suppose, for instance, new information, as you said, so basically they cannot anticipate, they may not be very good at predicting things like elections that no one can predict. Right? But the point is that if prices are currently computed, assuming certain projection of cash flows and then a signal comes so that expectations get coordinated in a different scenario, then you may have a very large change in the valuation of financial instruments. Let me elaborate on that. This is essentially at the moment, I'm not saying this has always been like that, but since a couple of years now, that is I think the way in which many people are thinking about transition risk.

So it's essentially the possibility of a change in expectations which triggers a revaluation of assets which could happen in the space of 3 hours and a half.

Riccardo
Understand. Thank you.

Emmanuel
So Stefano, before just in term of timing, I think you got 15 minutes, 20 max. So just for you to very good. Thank you.

Stefano
So, what are scenarios? Scenarios is important to realize that scenarios are not predictions of the future, they are description of plausible, possible plausible futures. And of course, in economics when we develop a model, we can always generate trajectories under scenarios. And that could be in many cases maybe an arbitrary choice. Common scenarios are not that arbitrary because they are generally constrained to they satisfy very hard constraint. One hard constraint is the fact that the cumulative emissions have to respect a certain carbon budget. So for instance, if a trajectory is associated with say a 2 degrees scenario, it means that overall the economy is not releasing to the atmosphere more CO2 than it would take to so it's only releasing the emissions that would lead to in total two degrees. You know that I don't know how many of you are familiar with this, but essentially there is a linear relationship, but a stochastic one between the cumulative emissions of CO2 and other greenhouse gases in the atmosphere and the temperature and the degrees of warming that the planet will experience. Unfortunately, we only know this in a probabilistic way, but essentially the more emissions, the more the warming.

And if you want to stay within two degrees of warming, you can only emit a maximum amount. That's the reason why the cumulative emissions that's why it's called carbon budget. So that's a constraint. This is not a constraint technology. You can't build solar gigawatt of capacity in solar power in the space of a night. It takes some time, it takes also money. But that money is actually not included in the scenario. We'll talk about that more in a moment. Most of the time these scenarios are generated with the so called large scale and process based integrated assessment models. And you see in this slide, there is a reference to a nice review of those. So there are two dimensions to the problem. On the one hand there is the physical risk and the other one is the transition risk. Why are they connected? Well, there is only transition risk because there is physical risk. Let me explain. Physical risk is associated with the fact of inaction. If we don't mitigate, if we fail to mitigate climate change we are going to proceed along the horizontal axis towards what can be called Hot house world. So this is a situation where which is the current trajectory by the way, we're going to exceed very likely 3.3 or certainly more than three degrees and depending on geopolitical developments could even be more.

In that scenario we're going to facing adverse impact of climate change. Actually the probability of for any degree of warming, the frequency and the magnitude of extreme event is going to increase. This is projecting, it's common to all projections but in that scenario so in here in the quadrant to the bottom right we are approaching also the possibility of tipping points although it's difficult to give a number or a probability on them to happen. But essentially these are also irreversible changes in the climate system. Then there is another dimension which is the one that I talked about the transition risk. As I said, we could do the low carbon transition in an orderly fashion but it can also happen in a disorderly fashion. For the reasons I explained, there are some dimensions that characterize all the different scenarios. And by the way, this chart has been one of the most important contribution I have to say of the network for Green, the financial system which is the platform of by now more than 90 or so financial supervisors around the world. So in the lack because we have to be clear about this, we do not have a quantitative and coherent way of treating physical risk and transition risk together.

That's not provided by the IPCC reports. The two dimensions are so impacts are evaluated, reviewed in the working group two and mitigation is reviewed in the working group three. But there is no formal and comprehensive integration of the two dimensions. This is very important. So what this chart is doing is trying to put these two dimensions qualitatively together because the very reason why we have transition risk is because there is a physical risk. If we could wait, if we could keep on emitting and we could delay the transition without particular damage, then we wouldn't have transition risk. But precisely because we have a limited time and we have to do the transition if we want to do the transition to stay within two degrees, we have to do it essentially by 2040, 2050. The more we wait, the more it increases the probability that that is going to happen in a disorderly way. Here in this line I have a description, this is taken from the guideline of the NGFS of the scenarios. It is a description of in terms of carbon price development and CO2. But I'll skip it for now. What I want to focus on is on an example.

What you see here on the left hand side is these are trajectories of what would be the output of the electricity sector for the technology of wind. So generation of electricity based on wind under different scenarios. So we see in the gray solid line is representing the production of electricity based on wind in a current policy scenario. So it's growing and it's growing because wind is becoming cheaper over time. But in a net zero 2050 which is a scenario where we reach net zero so carbon neutrality by 2050, of course that is increasing much faster. And the delayed transition is a scenario which overlaps entirely with the current party until 2030 and things start departing thereafter. The symmetrical is happening with electricity produced with gas. First of all, even in the current policy the production of gas is peaking in 2030. This is what we're representing here is Europe under one particular this trajectory produced with the remind model that's one, the one maintained at the peak in Potsdam. So there is a peak in 2030 for the production of electricity from gas. But if you compare that with what would happen in what would have to happen in a net zero scenario is clear that there is a dramatic shrinking of the output.

And now this is essentially the core of what I wanted to tell you today. Imagine that we are pricing the bond of electricity companies on the basis of a current policy and then say we've done this until yesterday and then say there is a signal that convinces as investors today that we are actually going to be very soon in a net zero 2050 scenario. Then we have to clearly adjust our valuation of the bonds of companies that are only doing wind and companies and bonds of companies that are only doing gas. And if there are companies that are doing a mixed of the two, well it will depend on the mix and we'll have to see the case. So it is precisely changing investors expectations over future production trajectories that can translate first of all in differences in profitability but also in differences in credit risk, then that is going to be dependent on the technology profile of the firm. So now let me give you just elaborate a little bit more on that and then I'll stop. What time do I have to stop to give you time to have like 15, 20 minutes for discussion?

Emmanuel
Normally it's now but no, you have five minutes. It's okay.

Stefano
I can take another five minutes. Okay, I'll basically be covering what I wanted to say. So the main issue is now to translate these scenarios into an adjustment of valuation. How are we going to do that? I'm going to give you now a visual representation and then I have some details if you're interested I can elaborate more on a framework to conduct a climate stress test. So on the one hand you need to have information at the counterparty level, what is the technological profile of firms and you can also I don't have time to elaborate too much, but I just want to say that you don't have to assume that companies are going to stay exactly as they are today. Of course company will evolve their technological share over time and that can be done, can be taken into account. Then you will have to compute the financial valuation of instruments such as equity and bonds first under the basement scenario and then under the transition scenario and then look at the difference. And I'm going to show you visually how to do that in a moment, but just want to now give you a sense of the different disciplines that are involved in this exercise.

It's important to realize it's an approach that is based on the balance sheet of the financial institutions, it is based on forward looking scenarios. So information about that's coming from science, about how the economy has to look like if you want to reach achieve certain targets. And so you have to incorporate scenarios on the climate policies into the trajectory of output, translate that into scenarios of transition which can be disorderly and as I said is related to change in expectations and then translate that into adjustment in risk measures. For instance, what is representing here distribution of losses and how it could be shifted to the left, for instance, resulting in a different value for the value of risk. And if you want to be a bit more sophisticated, there is also the network of banks and funds which are exposed to each other which can amplify these shocks. So what is represented here is how shocks may affect the balance sheet of banks and funds. And then in addition, there is a reverberation through the financial system which can eventually affect the creditors and the investors of financial institutions. There is a workflow on describing the type of data and the type of risk assessment that can be conducted in this framework.

And let me simply mention the fact that if you stay at counter you can conduct the analysis at the counterparty level for which you need counterparty data. Then you will adjust specific PD of counterparties, probability of default of an individual borrower and from there you can derive what is the portfolio risk of a financial institution. I will conclude with a visual illustration just to and then looking up for questions. So consider two scenarios. One, this is production of manufacturing of gas. Nowadays pretty popular activity under two scenarios. One that we can consider as a proxy of business as usual and another one which is decarbonization scenario. So you see that in the decarbonization the production of gas is decreasing sharply. And now what I want to highlight is the difference in output, in cumulative output. So the difference in cumulative output is precise, the difference between the two areas. So this area marked in red. Now let's make a very simple calculation, fictitious calculation, but useful, which is the following. The value of an equity instrument, as we know, is the discounted sum of dividends. Dividends are going to be a function of output, which in some extent has to be also function of the profits omega, which also be a function of output of the firm.

And you can compute the value of the equity under one scenario, under the transition scenario versus and as a relative difference with respect to the business as usual scenario. And this is going to be the shock.

Now, this shock in particular, if the discount rate is not too high, is going to be of the order of this cumulative difference, this difference in cumulative output. That's why this shock can be very large. It can be actually and I want to let me jump onto this example that it's an output we did for one member of the network for Greenery Financial System financial supervisor, we did this exercise recently. So with our framework, basically what you can compute is an adjustment in the probability of default of companies in different sectors, assuming in the most simplified case, the companies are active in one sector at a time. So, for instance, if they're manufacturing oil or gas or doing electricity from only gas or only coal and so on. And you can also model how their loss given default would change going from a baseline scenario to a transition scenario in red. And then, for instance, you can compute then what would be the variation in the value of the bond associated to such a company. So under some plausible, just to give you a sense of the order of magnitude and that'll conclude it's quite common that essentially you get differences of the order of 20, 40 or even more percent on the expected value of a bond.

And for equity it's even more in particular for companies that are focusing on fossil fuel. So then as a result and that I don't have the time to elaborate on that today. But then, given that each of the counterparties may be exposed to these type of changes in valuation, you can derive then how the risk measure can change for an investor that has these counterparties in their portfolio depending on then more complication which is in particular correlation you have to take into an account with co-polars and other things. So I'll stop here and happy to take questions.

Emmanuel
Thank you very much, Stefano. By now I pass to Riccardo. I'm sure that Riccardo got a lot of questions to ask. Ten to 15 minutes to do it. Yeah.

Riccardo
Thank you so much. I really like the presentation. Thank you so much. Can I ask you a question? Could you put up your slide 25. Is it possible to go back to the slides?

Stefano
Sure.

Riccardo
Because something that I would like to understand.

Stefano
There we are.

Riccardo
So the red area apart from discounting is a difference in valuation between the case when I value a particular firm assuming business as usual or total decarbonization, is that correct? Apart from discounting?

Stefano Battiston
Apart from discounting and apart from the coefficient that if you're assuming a perfect proportion dividends and out.

Riccardo
Indeed, indeed. But let's go back however, I don't know where the market is valuing at the moment because nothing is telling me that the market is valuing based on the business as usual. Perhaps plausibly the market give different weight to all these different scenarios. And again, I'm not a great fan of markets but I respect them a lot. When you come up with the adjustment, where do you assume the market is?

Stefano
Okay, that's a very good point. I think there is an argument to think that companies sorry that investors are right now probably assuming a baseline scenario as a business as usual. And it's the following that when you look at risk premium across companies that have very different technological profile. So if markets would be pricing in the difference in these scenarios in the future, then you would expect to see that a company that is very much into low carbon would have a very different risk premium than another one in high carbon. Unless maybe there is a complicated scenario.

Riccardo
There are two components. One is the expectation component, one is the risk premium component. For a risk premium component there is still a raging debate at the moment whether big losses are going to occur in high consumption states or in low consumption states. So there isn't even an agreement on what the sign of a climate risk premium should be. From a theoretical point of view and from the empirical point of view, calculating a risk premium basically climate change has been on the radar in the last 20 years. You can't do a statistical study of risk premium with 20 years worth of data. 50 years are absolute mean. You need several cycles. One thing is expectations from risk premium. I really don't know what we can say and in expectations what is telling me what the market is expecting.

Stefano
So what do you conclude from the fact that risk premium but also cost of capital. So essentially cost of capital, whatever is the metric that you're using. Essentially the risk that markets are associating to companies that are high carbon and low carbon is very similar, few basis points depending on the specific context. Now how do you reconcile the fact that companies that are very different which are supposed to perform very differently under these scenarios? How can it be that they are associated?

Riccardo
That's a good point. Now I understand. Now I understand what you're saying. I understand rather if I look at the cost of capital and you're telling me that the cost of capital is minute for this a much stronger point. Thank you very much.

Stefano
But I agree with you that it's always possible that maybe markets are right because they are anticipate that there will be I don't know, and I'm making it now on a stream case. Maybe there will be some form of bailing out those companies that are that scenario. It's actually correct.

Riccardo
But they will never happen, would it?

Stefano
Well, I think this relates to a similar fundamental question. If you look at CDS spreads in 2007, they were at historical low. And you could argue, we know what happened afterwards. CDS were reflecting the provisional default of the financial institution. What would say, okay, they were completely off. But at the same time, you could also say how many financial institutions really defaulted compared to what happened afterwards. You could also say, you see.

Riccardo
They were right.

Stefano
They were right because they were anticipating that things would not be so disruptive.

Riccardo
Yeah. And there was another observation that I had at the beginning. You showed a very I think it was your slide, one of your first slides, slides two, and it was the mismatch between the actual investment needed and the actual investment carried out for the different types of transition. Now, if I'm correct, they refer to electricity, is that right? Because electricity is only about 20% of the 20, 30% of the energy consumption. So your point is even stronger if you put in all the transition. Net emission technologies are going to take a lot of energy, lots of other things on top of electricity. Yeah, even the one before the slide before.

Stefano
Now, this slide is investment need 3 to 6 times is for all sectors.

Riccardo
All sectors. Yes, but to do what? Because in order to decarbonize because ...

Stefano
There's energy efficiency, electrification of transport, and then changing the source of electricity generation.

Riccardo
But for instance, the bulk of our energy use goes into cement, steel, plastic and fertilizers. Where would this go?

Stefano
That's where it goes.

Riccardo
This classification here, aren't we missing some big elements? I don't know.

Stefano
So your question how you compare this with the statistics of the flows of energy.

Riccardo
Yeah.

Stefano
Well, okay, but the investment needs are maybe not, you see, they may not be proportional to the.

Riccardo
What I'm trying to say. For instance, a huge amount of energy goes into cement for buildings. Where do I find this in energy efficiency, transport, electricity and agriculture, a huge amount of energy goes into plastic in fertilizers, which seem to be missing there. So all I'm saying is I think your point is even stronger because there are other elements, unless I am misreading this chart, where is cement that is needed for building in that?

Stefano
Oh, I see, okay, I see your point. So I was not responsible for this specific part. So I will have to check that where cement is specifically, I completely agree. So cement and you can do.

Riccardo
Steel is big, steel is big.

Stefano
Both cement and steel, you can do it. So the amount of CO2 emissions involved with it depend a lot with the type of technology you use. And there are ways of doing steel by reducing quite a lot the emissions, for instance, for the furnaces, that can has to do with the electrification of the steel production. So that would be a thing that would go in here. For cement, no, because part of it is just the clinker production. And that is probably so maybe this is not directly specifically involved in that.

Riccardo
But this will strengthen your point. This will strengthen your point. My observation would make your point even stronger.

Stefano
Okay, yeah.

Riccardo
Because I'm saying there are many other areas here in which I expect that this thing is, thank you.

Stefano
No, thank you for this. It's a very important and correct observation. I don't know the figures on how much investment would be needed in order to turn the current cement production into low carbon. That is the question. Yeah, I think it's a very relevant question. I don't know the figure right now.

Riccardo
I know that Yann had very good questions, so I have more things, but I want to give a word to Yann as well.

Yann Lancernon
Sure. First, thank you, Stefano, for the presentation. I mean, you talked us through that very well. It was very easy to follow. So I had a few questions ready, but actually I have a new one that I quite like. So you mentioned that the electricity generation by renewables will have to reach around 90% to have a transition. Do you know how do orderly models account for the intermittency factor of renewables such as wind and solar? What energy do you need with renewables to account for the intermittency factor? So do you need a firm energy source? I'm thinking of nuclear. Or do you need a lot of battery storage, for example?

Stefano
Well, this is a question for the engineers who are developing these models and the trajectories. So my understanding is that there is a range of technologies that allows to cope with the intermittency of renewables. And one is you can do hydro buffer, hydro pumping. If the landscape of the country allows for that, then there is, of course, electric batteries. There is the technology of electricity to gas. So you can use renewable to generate synthetic gas or even hydrogen, which you can store as long as you want and then reuse when there is a lack of sun or wind. In those countries who have the technology. There is of course, also the nuclear energy that can be used as a complement. Not all countries have chosen to take that path. But at the moment, my understanding is that what the community of energy scientists are saying is that it is possible to leverage on renewable energy and cope with the intermittency even without nuclear you don't need the nuclear energy to have that. So it's feasible to ramp up the share of renewable energy essentially with existing technologies, of course, has to be some investment. And then there will be things will depend on case by case, there will be countries where no other solution is possible. Because if a country is completely flat and they don't have other solutions, then.

Yann
Yeah, sure, I understand. All right, thank you. And second one, and then I will let you go. Do you have any estimation of a carbon price that is required worldwide to have an orderly transition as of today?

Stefano
No, but the level of carbon price so I have something here. So it's not that the carbon price so, first of all, the carbon price is dependent on time, right? So it's not that specific. It's a very good question, but the answer is a bit more complicated because it's not that a single price would guarantee an orderly transition in case it would be a schedule. And again, this is not my work. This is the scenarios generated with the integrated models. And I can say a number of limitations when you look at these trajectories. So in those models, what you typically have as the only comma policy is the carbon price. The schedule of the carbon price is computed by means of intertempal optimization of consumption under the constraint of reaching respecting the particular carbon budget.

Yann
Okay.

Stefano
Now, if you want to reach, for instance, something below two degrees. So you have, like, in the orderly I think it's the below two degrees in the orderly one would be at 1.5 is the blue one here. So it shows that you have a very high carbon price towards the end of the century. Actually, the oil is until 2050.

So it's already 2050. We're talking about $600. I am not defending these numbers, I'm just reporting them. However, what I would like to point your attention to is that in these models, the carbon price is the only climate policy and other climate policy we can also think of other climate policies you could have. So we know, for instance, that you could ban certain technologies, for instance, the dirtiest one, like coal. And if you think about this is not unconceivable because the reason why everybody in Europe is now driving a Euro six engine is simply because the previous ones are not allowed to go to drive into the cities. That's more regulation than simply carbon price. Not to say that carbon price are definitely and market based solutions are definitely needed and useful, but they don't need to be the only one. So the effect can be amplified by the combination of these policies with other policies.

Yann
Thank you. Yes. So multiple policies could intervene rather than just carbon price. Thank you, Stefano.

Emmanuel
Okay. Thank you, Yann. Thank you, Ricardo. I have a question which is on my side related to the previous discussion. Do you think that transition risk is edgeable or non edgeable? Is it something you think that there is a possibility to find some instrument to edge this risk or this is a risk which is non escapable and there are no solutions?

Stefano
Well, I think you were referring probably to the kind of aging climate risk other papers that colleagues like angel and others have been working on. But in the end, if you look at their result, what is the portfolio that is hedging climate risk? It's a low carbon one. So in the end the solution for hedging climati risk is having a portfolio that is sufficiently exposed to those firms that are engaging in the low carbon transition. And I would say even in a you could imagine that really the world continues in a business as usual and therefore complete inaction. And one would have to look at how bad though that portfolio would perform. Probably wouldn't perform much worse than a standard portfolio. But of course, I'm now speculating this is ongoing work that has to be basically backed up with the benchmarking and validation that is still ongoing.

Emmanuel
Thank you.

Stefano
My short answer is a portfolio that is diversified enough and covering enough low carbon technology. That would be my bet for a good hedge.

Emmanuel
Thanks. I see Ricardo that want to

Riccardo
Emmanuel, do we have time for a last question?

Emmanuel
Yes, you have time Ricardo.

Riccardo
Can you go to slide 25 again, which is my favorite and since I am an asset pricing guy.

Stefano
Sorry.

Riccardo
Because to me it's very exciting. This there it is. So my previous objection was to say we don't really know where the market is actually pricing. So would it be conceivable to do the following? You have a market capitalization for a particular sector given your approach, you have the cash flows and the profits and therefore the value of the equity in different scenarios. So you could work out the implied probabilities that give you the price of a market and with several sectors you could find the set of probabilities that recover the market prices today. And you have a very powerful equation down here and then I've seen it is made more clear. So it gives you the value of the equity as a function of the cash flows and you say I know the cash flows as a function of the different scenarios.

Stefano
Yeah.

Riccardo
So if I know the market capitalization today and I have many sectors, then I can find a set of probabilities that best recover the market prices and then we know what the market is. Actually, you may say is giving a 5% probability to this scenario. 10%. That would be fantastic.

Stefano
Beautiful idea. I have a concern, I am not convinced that maybe there is an indetermination issue because you would need to determine a vector of probability.

Riccardo
Yeah, but I have many sectors. You have more sectors than probabilities because you are powerful, because you can do this not just by scenario, but also by sector. That's what really excites me. So I would have market capitalization for steel makers, for transport, for wind turbine, for the blah blah, et cetera, et cetera. And supposedly investors are equally bullish or negative in a homogeneous way. It's not that investors instill a set of beliefs.

Stefano
Okay, then, yes.

Riccardo
Then that would be really cool.

Stefano
Maybe another little constraint that would have to be put is that there is no variation on let's say everybody is only thinking of two scenarios. One, business usual and.

Riccardo
No, but you can do it in your equations further down for all the scenarios.

Stefano
Right, but if the number of scenarios that is variable, or if we don't know the number of scenarios.

Riccardo
That okay. No, we have to fix the number of scenarios. We have to fix the number of scenarios.

Stefano
If we don't know how many scenarios, we don't know how many are those, the probability that we want.

Riccardo
In your scenarios span a really wide range of outcomes. I really liked it. It goes from super fast to Trump gets reelected effectively and he gets almost everything in between.

Emmanuel
Ok, I think we can continue the discussion so effectively. Very exciting. So thank you. Thank you. First Ricardo. Thank you, Yann. And most importantly, thank you, Stefano, for this great presentation. Just a reminder, today presentations or today's slides and the recording will be posted on our dedicated web page. And before we close down, I just want to take a moment to mention that this was our last session for this academic year, but we will be back, we'll be back in September with new great presenters for new season. So stay tuned and see you soon. Thank you very much. And Stefano, hope to see you physically on the ground in Nice. Thank you.

Stefano
Thank you for the question. Excellent.

Emmanuel
Thank you.

Make an impact
EDHEC BUSINESS SCHOOL
[Music]

 

[Music]
EDHEC Business School
EDHEC Speaker Series - The future of Finance
DeFi: The Future of Finance?

Campbell Harvey
Professor of Finance,
Duke University

Thursday, 31 March 2022
6 P.M - 7 P.M Paris time

Emmanuel Jurczenko
Okay, so let's go ahead. So hello everyone, and welcome to our new EDHEC virtual Speaker Series on the Future of Finance. So I'm Emmanuel Jurczenko. I'm the director of the Graduate finance program here at EDHEC. And it's my pleasure to welcome you today. So, first, I want to introduce our co-moderators of today's session. We have Raman Uppal, which is a professor of Finance here at EDHEC. He's an expert on asset pricing model and continuous time finance. We have also Romain Poher. Romain is a first year student in our MiM - Master in Management Finance Track Program. And it's my great pleasure to introduce our guest speaker for today's session, so Campbell Harvey. So Cam is professor of Finance at Fuqua School of Business, Duke University. So, Cam asked me not to give a large bio, but I just want to say that Cam is a rendon financial economist. He have published more than 150 scholarly articles, big range of topics going from term structure to emerging market finance, commodity investing, higher dominance. And what interests us today is that over the past seven years or so, cam adventure in crypto, asset and digital finance. He got a course on blockchain technology and DeFi at Duke University.

And it got very interesting and popular coursera online courses on DeFi infrastructure and blockchain business models. And he co-authored recently a new book, which is a must read on DeFi and the future of finance. And in fact, I take the title of our speaker series, the Future of Finance. I take an inspiration of the title of Cam book. So we are very fortunate to have Cam. Thank you very much. The virtual floor is yours. Thank you. And now we are here to learn more about the risk and the opportunities of DeFi.

Campbell Harvey
Thank you very much for inviting me. And I'm just about to share my screen, and hopefully you can see the full screen. So let me talk about DeFi. Yes, I've been into this space for quite a while. I think it's exciting. But in finance, we always look at not just the expected return, but the risks. And today I'll talk about some of the problems that DeFi solves, but I will also focus on the risks. So times have definitely changed in the space. The CEO of the largest bank in the JP. Morgan, once said that Bitcoin was like a fraud, which was really hard to parse, given that Bitcoin is just a computer program that nobody really owns, just runs. And indeed, he even went further to say that any Morgan employee caught trading Bitcoin would be fired for being stupid. Of course, things have changed. So Bitcoin is traded by JP. Morgan along with other cryptocurrencies. Indeed, we saw the most successful company in this space, Coinbase, which is a centralized exchange debut with an IPO that fetched $85 billion. And this was very good news for Duke University because we were early investors in Coinbase, and indeed, we've got quite a robust crypto venture portfolio for our endowment. Okay? But this is a very important message that I want to convey that way too much attention is paid to Bitcoin, and way too much attention is paid to whatever Elon Musk tweets about dogecoin. So there's something else that is largely under the radar, at least of the popular media, and it's decentralized finance. And this is quite exciting in terms of the problems that it solves, which I will go through, but it's also quite challenging indeed. I start my course with this word cloud, and many of the words you kind of know already, like gas or vault or hash.

But all of these have very special meanings in decentralized finance. And by the end of my course, I put a checkmark beside each one of these words. So what is decentralized finance? And there's the COVID of my book. So think of decentralized finance as a way for peers to interact directly without a middle layer. A simple example of this is a decentralized exchange where I've got asset A, I want asset B, I send asset A to an algorithm, which is called a smart contract, and the algorithm sends back asset B. Effectively, this is matching peers. There's no broker, there's no centralized exchange. It's very efficient. Indeed it's more than exchange. It has to do with savings and savings rates, borrowing. And I'll talk about a little bit at the end this idea of tokenization. So this is a vast space, and again, much of this is under the radar, and I want to put it over the radar. So first let's talk about the problems that DeFi solves. And number one is inefficiency, and this is something I feature in my course. It's one of the first money transfers, Western Union transfer from 1873, and it's for $300.

What I want you to take a look at is the fee associated with that transfer 150 years ago. It's $9. So it's very similar to, like, a credit card swipe today. And technology has advanced since 1873, you know cash is delivered in a sack via like a horse, but it's still 3% today. Indeed, I put this in my book, but then I got attacked on social media for being inaccurate, and I actually went through the Western Union online system, and I want to send $300. And you can see that the fee here is $47. So it's even worse than in 1873.

So this is just an example of an inefficiency that is completely unacceptable today. And sending money is slow, it's insecure, it is very costly to do, and the cryptocurrencies actually deliver something that can improve upon that. So there's also, like, many other things here. For example, to settle a stock transaction. So that means when you buy a stock to put it into your name, that takes two days. In this space, there's no difference between the actual trade and the settlement. It happens simultaneously in decentralized finance. So limited access is something that is important to me as a problem in the world today, especially given my academic research on emerging markets. And it is a fact that about 1.7 billion people are unbanked today. And many more are underbanked. And we don't have the exact number of underbanked. It's hard to actually calculate. But let me give you an example of an underbanking situation. You're an entrepreneur. You've got a great idea. You've calculated that the expected rate of return is well over 20%. You go to your bank. So you are banked and you ask for a loan to get this idea to reality. And the banker says, well, this is a good idea. I think this will be successful, but we would prefer to deal with one large customer rather than 100 customers like you. But given you already bank with us, given that you've got a credit card with us, we will extend the credit limit on your credit card and allow you to borrow the money that you need to fund this business. And we know what that rate is on the credit card. It could be well over 20%.

And what does that mean? It means the project is not pursued. And these are the type of projects. That we need to pursue in our economy. The US. Economy is stuck in 2% real GDP growth. European economies less than that, 1%. We've racked up a lot of debt, and the best way to pay that off is with economic growth.

And this particular space holds the promise of reducing these frictions. And another strand of my academic research shows that there's a direct relation between reducing frictions and increasing economic growth. So that project that was promising, let's say 25% rate of return, that is the type of project that generates economic growth. So opacity is another issue. And this is a letter from Senator Elizabeth Warren to the Secretary of the treasury. And she says "DeFi refers to a fast-growing and highly opaque corner of the cryptocurrency market".

Well, this demonstrates that Senator Warren or whoever wrote this letter doesn't understand decentralized finance because in decentralized finance, everything is transparent. So these algorithms or smart contracts are completely open source. Anybody can see. You see the balances of people. So if you're going to send me 100, I can see your balance and verify that you've got the 100 to send to me. So it's actually our current system of centralized finance that is opaque. We rely upon our regulators to make sure that our financial institutions aren't taking too much risk. And these regulators have a dubious track record to say the best. And I'm not talking about going back to the Great Depression. I'm talking about just 15 years ago with the global financial crisis.

So obviously, there's a lot of centralization in our system today. And the main point here is that our financial institutions are centralized and they have significant market power. And when you have market power, it means that savings rates are lower than they should be and borrowing rates are higher than they should be. The last issue is the lack of interoperability. And in traditional finance, let's say you want to buy some stock, so you set up a brokerage account and then you need to wire transfer from your bank money into the brokerage account that could take two to five days before you're ready to go.

In decentralized finance, it's completely different. You've got a wallet and the wallet is effectively your bank. So you have a smartphone, you've got a bank, and you connect your wallet to the decentralized exchange, you're ready to go. That's it.

And this is also a great feature of web Three. So DeFi is a necessary condition for web 3. In web 3, you go to a site, you connect your wallet, there's no username, there's no password, you connect your wallet and you can pay, or be paid seamlessly and immediately. So interoperability is a great feature of this space.

Of course, there's risk and there's lots of risk, and I'm only today going to highlight some of the risks because I do want to reserve time for questions. So smart contract risk is perhaps the most important and it is a new attack vector. Think about a hacker trying to get into a commercial site and it is really difficult to get in.

So most of the work is to get into that site and then once you're there, you have to deal with millions of lines of code to figure out exactly where to go to get what you want.

In this space, anyone can view the code, it's completely open. So you could have 100 hackers looking at the same code, trying to have some sort of exploit. And this is called smart contract risk. And indeed, there's many companies that their business plan, their business model is just to audit these contracts before they're deployed in decentralized finance. So there's many types of errors and one type of error could be a logic error, another an economic exploit. So logic error is really simple, but it's remarkable that these errors are made. For example, a contract might have 13.9999 Ether in it and there's a command to pay out 14. So effectively you round up to 14, but 14 does not equal 13.9999. Even though the difference might be only a few cents, the command fails and this could lock money in a smart contract. So you need to be careful of logic errors. Economic exploits are even more complicated. It might be that a smart contract uses information from outside a blockchain, so that information might be a price at an exchange. And if that exchange is relatively illiquid, an attacker might do large orders on that illiquid exchange to manipulate the price. Once the price is manipulated, then that can cause havoc in the actual contract. So economic exploits are much more challenging to deal with. Let me talk about something that is really fascinating to me and indeed maybe the most fascinating aspect of decentralized finance, and that has to do with a flash loan. So a flash loan is a very interesting loan in that it's completely uncollateralized.

There's no counterparty risk, there's no duration, and there's often no interest rate. So all that sounds completely crazy. But let me explain how it actually works. So, in decentralized finance, a transaction can have many steps and every step needs to work, or the contract or the transaction fails and you go back to the state of the world before the transaction. So let me give you an example. Let's say you see on two different exchanges, the price of a token is different. So step one in the transaction is to borrow some money, and this is the flash loan.

Step two is to use that money to buy the cheaper priced token on whatever exchange.

Step three is to sell that token on another exchange for a higher price.

Step four, pay back the flash loan.

And step five, pocket whatever profit you make.

So the key thing here is, suppose something happens where you go through the first couple of steps, you buy the token cheaply, but then on the third step, the price that you're able to sell it at, instead of being higher, it's lower. So the price has changed. Therefore you don't have enough money to pay back your flash loan. Therefore the entire transaction fails and it's like nothing happened.

So that's why it's zero duration, it's zero counterparty risk. It is a remarkable idea. And this is one, and I highlight this, the very first line here is more than five. It's more complicated a transaction. But notice the very first line is the flash loan for the equivalent of 200 million. And you think in today's world of finance, who can do an uncollateralized loan for $200 million? Maybe like one of the largest hedge funds in the world. But that's about it.

This could be anybody. This could be like a high school student and she sees an opportunity and she borrows $200 million to do this arbitrage with no risk. So it's a very interesting idea. So there's other things that happen. And this is 611 million stolen from Polly. What was interesting in this particular exploit is that there's a conversation with the exploiter. And let me just highlight that conversation. So basically, people are asking, well, why did you do this? And the exploiter says, Well, I spotted this bug and had mixed feelings. What to do? What would you do?

You see a huge opportunity. Do you contact the project team and say, oh, well, there's a problem with your code and do you ask them to fix it? But anybody on the project team could be a trader. Given the 1 billion this person is rounding up, 611 million to 1 billion, I can trust nobody. The only solution I can come up with is saving it in a trusted account and keeping myself anonymous.

So it's really interesting what happened here. The money was returned. So this hacker was known as Mr. White Hat, and the money was returned and actually there was a bug bounty paid to the hacker. And this is actually pretty popular that many of these protocols have very generous bug bounty. And the hacker or exploiter basically made the decision, do I want the authorities chasing me the rest of my life or should I take the bug bounty and he or she took the bug bounty? Okay, so not all contracts are smart. Indeed, in finance, if I see the adjective smart in front of any product, I'm very skeptical.

Okay, what about governance risk? So it's another type of risk. So there are some contracts that are just set in stone, so once they're deployed, you can't change them. But other DeFi applications rely upon a governance mechanism to set certain key parameters. And these are controlled by a DAO, which is a decentralized autonomous organization. So there's governance token and it is possible that somebody could amass enough governance token to do something nefarious. And this is an example of something that happened last year. And this is true senior edge dollar. And often when a protocol launches, the developers retain control for a while until the bugs can be ironed out.

In this particular situation, the developers only had 9% of the dow, and somebody amassed much more than that. And then they put a motion forward to mint themselves 11.5 quintillion of these tokens. It passes and then the hacker dumped 11.8 on a decentralized exchange and drove the value of the token to zero. And I've got the Twitter thread from the developers and what's most interesting is the very last line that essentially says, well, this is sad for us, but this is how it works in decentralized finance. You get control, then you got control.

Oracle risk, I'm going to skip over because I basically just gave an example of a situation where a smart contract needed some information from outside the blockchain. And that was the price at a decentralized exchange or a centralized that that mechanism to get the information from outside the blockchain is called Oracle. And again, one vector of Oracle risk is where, for example, that exchange is manipulated. But there's other issues with Oracles and it is a significant challenge in decentralized finance. For example, if the Oracle goes down. So there's an outage in the Oracle that means that any transaction that calls the Oracle will fail. So this again is a significant issue and we have had these outages. So another risk is scaling risk. So Bitcoin does five transactions per second. Maybe Ethereum does 15 transactions per second, whereas Visa and Mastercard does well over 50 transactions per second.

So how can this space compete? And right now it can't. And it's really important that right now it doesn't have the speed to deal with competition of centralized finance, but you need to judge the space by the future. So in decentralized finance, most of it is in the Ethereum blockchain. And Ethereum uses a very computationally intensive consensus mechanism like Bitcoin does, called proof of work. And it is very slow. Ethereum is moving to a different mechanism called proof of stake. And that mechanism uses about as much power as Visa or Mastercard would. And this allows for much faster transaction. There's also scaling and various degrees of decentralization. So there's many competitors to Ethereum's layer one chain right now and some of them use proof of stake. Some of them are just more centralized so that they've got a smaller number of validators. And this allows them to get up to 50,000 transactions per second, which is competitive with centralized finance. Ethereum is not going that route of vertical scaling. They're going to do horizontal, which means they will break their blockchain into 64 different shards and then have a master chain called Beacon that controls those shards.

And this will greatly increase speed. And again, that alone could be 50,000. And then there's Layer 2. And these are secure areas or channels where you have an on-chain transaction to seed the channel. And then you can essentially do an arbitrary number of usually small transactions with minimal fee almost immediately. And then at some later stage, you write to the Master chain, the layer one chain. So again, this offers a very large increase in transaction per second. So in my opinion, this is a risk right now, but this is the type of risk that can be mitigated. We have these new ways to do exchange with a decentralized exchange. And you've got algorithms that effectively you put the information in as to what you want to buy and how much and it's telling you what the price will be. These decentralized exchanges, they operate 24/7. They're just algorithms. They don't care if you're a buyer or seller. It's really very interesting. But of course there are risks. Like a flawed smart contract. Illiquidity is also a risk that's important. And it's just like regular centralized finance. If you've got an illiquid market, that means it's going to be very costly to transact a decentralized exchange if it doesn't have enough liquidity, that is going to be an exchange that there's going to be a lot of market impact and transactions costs.

Okay, well, custodial risk, that's something that is very unique in the space. So a cryptocurrency is defined by its private key. Private key is a random number, a large random number, so 256 bits. And you go from a private key through elliptic curve mathematics to a public key. And it's a one way operation. And the public key can be made public, but people with a public key can't go the other way. So they can't derive your private key from the public key.

So custodial risk is when you lose your private key. So there have been many situations like this, but maybe the most famous was a New York Times story of a developer that thought he was being smart by putting all of his private keys in a hardware wallet that was not connected to the internet. And the hardware wallet is password protected. But then he forgot the password. And the way the hardware wallet works is that you get ten misses of the password. So on the 10th miss in a row, then the hardware destroys the data and this person has tried eight times and failed eight times. So two more tries and in the wallet is 220,000,000 of Bitcoin. So again, this is an issue. Again, there are solutions that are arising. So there are companies like Coinbase Pro or even Fidelity that will custody for you. So you don't need to worry about losing your private keys. But this is all a new space, environmental risk.

So this is a big deal where you've got Bitcoin using the equivalent amount of energy as the country of Argentina. So proof of work has got many advantages. It's computationally intensive to such a degree that the Bitcoin and Ethereum blockchain is effectively unhackable by a hacker or even a country. It's just way too difficult to amass that computing power. And it's very specialized power, so it makes it very secure, more secure than anything we've ever had in history. But it is a technology that takes a lot of energy. And proof of work in particular is pollutive in terms of carbon. Indeed, I've actually calculated the carbon cost of generating one new Bitcoin and it's like $4,000. So if you bought the offset, it'd be about $4,000 and that's a lot.

So it is tricky though, if you trade Bitcoins, it doesn't make sense to pay a $4,000 tax for every transaction. And also the Bitcoins are fungible. So the early Bitcoins, the carbon cost was it could be just a few cents. So it is a challenge, but nevertheless, this is a significant hurdle. But in decentralized finance we use Ethereum and Ethereum like blockchains and Ethereum switching to proof of stake.

So in the Bitcoin blockchain you can't do any smart contracting. So it's not really used in decentralized finance other than a store of value. And we can tokenize Bitcoin. I think for decentralized finance the environmental risk is pretty low and it's really a question of when Ethereum will make the transition. And they've already tested the Beacon Chain, I expect 2023. For investors, you can already buy Bitcoin ETFs that do the carbon offset. Regulatory risk is a big deal and DeFi has been in the cross-hairs of the SEC and the US. And it's really the case right now that we don't have regulatory guidance. In the US, many things are banned. So this is FTX and notice that it's not available to anybody in the US. And a few other countries, like North Korea.

So my opinion is the following that it's really difficult for the regulator. The regulator is too harsh. Then all of the innovation goes offshore and you don't want your best ideas for any country to have to flee the country. But if it's too lax, then people get exploited. So you need to find the middle ground and to complicate it even more, the technology is complex. So you need to invest in learning about this technology.

And indeed, the regulator needs to make that investment. So regulator is not taking a master's at Top university. The regulators got a full time job but they got to learn this stuff and then even if they learn it. They realize that what they've learned depreciates very quickly and new stuff is arising all the time.

So it's really difficult. So let me conclude and take some questions. So in my opinion, we've been operating effectively with the same model of centralized finance for over a century. The current wave of fintech like stripe and companies like that just improves the user experience, which is great, but it uses the centralized architecture and in my opinion is fleeting indeed. This is a quote from a speaker of my course. The current fintech like stripe and plaid is like putting lipstick on a pig. And what he means is you can only go so far because using that centralized architecture, it's really early in this space, very early, but already this space, the capitalization, is $2.2 trillion. It can't be ignored. This is a quote from a highly respected professor at Columbia University in the engineering school. And I wanted to read it to you because it's very powerful. Future generations will be jealous of your opportunity to get in on the ground floor of this new era. Okay? So that should be something that you need to reflect upon. I wish I could turn the clock back and to be in your position today. This is an enormous opportunity. So to be clear, this is not a renovation of our current system.

It is a rebuild from the bottom up. And we're just seeing the scaffolding of a new city. My book begins with the sentence "we have come full circle". And what I mean by that is we actually started thousands of years ago with decentralized finance, and that was the barter system. And the barter system was very inefficient. And markets became much more efficient when money was introduced. Well, now money has been redefined and money can be various different tokens. And these tokens might represent gold, the token might represent land, the token might represent apple stock.

You've got all these different ways to store and to be paid and to pay. And really what this is is the return to barter, but in a much more efficient way. I believe all assets, physical and virtual will be tokenized in the future. And let me end finally. I hear this all the time. People say, well, this is complicated, and we think it's a bubble, and we've got zero exposure to crypto, and it might be an investment manager with a portfolio. No, we're not participating. We've got zero exposure.

Wrong. You've got negative exposure. So, in finance, we would call this negative beta. So some of the stocks that you're invested in, they're in the crosshairs of decentralized finance, and they could be disrupted.

Indeed, your human capital could be disrupted also, if you are tied to one of these companies, working for one of these companies, that will be disrupted in the future. So you need to reflect upon that, that even though you're not participating, you're effectively having a negative exposure. Okay, so that's my book, and I'm really looking forward to your questions.

Emmanuel
Thank you very much, Cam. Great presentation. So now we'll turn to Romain. But just before, I just want to remind the audience that you can use the Q&A chat box. We already received two questions. First, we'll start with Romain, and after, Raman will jump in, and we'll take some questions after. Thank you, Romain.

Romain Poher
Thank you, Campbell, for this really valuable information. And it was really interesting. The first question that comes to my mind would be that given that the security of the technology is based on the difficulty of calculating blocks, what about the risk represented by the development of quantum computers? I know that traditional finance is also exposed.

Campbell
Yeah, no, I understand the question. Indeed. The other course I teach is called Tech Driven Transformation a business and we do not just blockchain technology, but quantum computing. So I'm very familiar with quantum computing, and you get some people that have a little bit of knowledge about the space, and they say, oh, well, quantum computing is a real threat. So for the proof of work, currencies like Bitcoin and Ethereum right now, it'll basically make all the mining irrelevant. And it just shows that they don't understand quantum computing. So, for the cryptographic hashing that goes behind the mining, the quantum computer is not useful. That is not the threat.

That threat has to do with the public key, private key. So, remember I said that the private key is a long, random number. You put it through an elliptic curve algorithm, and you get a public key. And the idea is, that's a one way operation. However, the quantum computer has the potential of taking that public key and going the other way. And it's not just potential. It will be able to do that in the future. And then you might think, oh, well, this is a disaster to the space because all these public keys basically will be reversed, and the people with a quantum computer will grab all the currency, and it's just not the case. So, when this gets close, what we'll do is I will take my Bitcoin for example, and I will set up another address, and I will send it to myself, left pocket to right pocket, and I will sign it with a quantum proof signature.

And these quantum proof or quantum resistant signatures, they already exist today, so I'm not worried about that at all. But it is interesting to me that the people that have got all these quantum computer firms, they don't see the upside here. The upside is not taking my Bitcoin or my Ethereum, it's all the lost Bitcoin. And there could be like 3-4 million of lost Bitcoin and that can be recovered and nobody can claim it, right? So it might be that there's 100 Bitcoin that's recovered and some person says, well, that was really mine, I lost the key.

Well, it's impossible to prove, right? So the only way you can prove ownership is with the key. So quantum is not a threat.

Romain
Thank you for this really precise response. And a second question would be, it looks like decentralized finance, and most importantly, cryptocurrency are very interesting when it comes to speculate on their value. However, I don't figure out how they can truly improve the real economy. What's your insights on this? You told us a lot.

Campbell
Yeah. In economics, economists disagree upon almost everything, but there's a few things they agree upon. And one of those things is if you reduce frictions, that's a good thing. So think of reducing transactions costs, that's a good thing. And that is something, again, in my academic research in the Journal of Financial Economics, I've shown that reducing these frictions has a direct relation to improving economic growth. I'll give you a simple example. So the situation that I sketched earlier, where the loan rate was so high that the entrepreneur didn't pursue the project.

Well, if you reduce that loan rate, then the project's pursued and that causes increased employment, increased investment, increased GDP growth. So there's a direct relation here, but this is not just doing more efficient exchange. So decentralized finance goes well beyond that. So right now, if you put your money in a commercial bank, in a savings account, you're losing money after inflation. So you can deposit your money and you don't need to invest in a risky cryptocurrency. You can use a stable coin that's linked to the euro or linked to the dollar. You could deposit that in decentralized finance and earn a rate of return that compensates the least for inflation. And you might say, well, how's that possible? Well, it's possible for a number of reasons. Well, number one, there's no fixed costs of like, a commercial bank, the brick and mortar, all the employees, the security, the dividend that has to be paid to the shirt, there's nothing like that. So it kind of makes sense that savings rates are higher and borrowing rates are actually lower. It's also the case that many of these protocols, and this is a very cool aspect of decentralized finance.

What often happens is you reward people for using your protocol. So think of it as in traditional finance. You go buy an iPhone and as a bonus you get a share of Apple stock. That's kind of what's going on. So you make some interest, you make a reward also. This is, this is a good thing in terms of the economy. So if we could have systems where we reduce these costs, where we have a system where we've got financial inclusion. Where everybody is part of the system. So it doesn't matter who you are, you don't need to be a person that has got an account at a central bank. You don't need a credit card, you just need a wallet. And that just means you need a smartphone. So everybody is included. All of that is positive for economic growth. And this is not just a situation for a country in Europe or a country in North America. This is a global idea and this is a way to basically include people in the financial system that are excluded today. So our system is exclusive today, not inclusive. This technology is fundamentally about financial democracy and we haven't had that in a long time.

Romain
Thank you. Thank you a lot for your answer. And maybe if I can ask you a last question about stable coins. As I understood them, they are indexed on other assets. So if I want to convert my stable coins, the brokers are supposed to be able to deliver the indexed assets in exchange of my stable coin before removing the coin from the market. So if the assets used as a proxy is exchanged on a centralized market, my stablecoin becomes indirectly centralized too. Then what is the point of buying a stable coin rather than the assets directly?

Campbell
Okay, so I would also, if you're interested in this, refer you to my coursera or my book where I spend a lot more time than just a few minutes answering your question. So why would you want a stablecoin like USDC, US Dollar Coin? Because it is centralized. As you said, there is a company that runs this and they vault your US dollars and then issue a token. Well, one reason to have that is that with that USDC you can send that all over the world instantly. Okay? So there's no delay, there's no two days, it's instant.

The second reason is that you can deposit it in a decentralized protocol and earn some interest. So indeed, you take for example, your savings deposit, that's earning like zero in, let's say, US bank. You take that cash, translate it into USDC, and then deploy the USDC and make 6 or 7% interest. So that's a good reason to do it. However, this is really important. There are many different types of stablecoins. So what you're talking about is a centralized stablecoin that's used in DeFi because there's a company behind it.

What I'm more interested in are decentralized stablecoins. So these exist there's no company, it's just a protocol and they are collateralized with other cryptocurrencies over collateralized and are pegged to whatever you want. So it could be a dollar, it could be anything arbitrary as long as it's over collateralized. So those don't suffer from the issues that you've mentioned. And some good examples of these stablecoins that are decentralized indeed. The first one is the MakerDAO's, DAI. And one that's very interesting to me today is Fei, FEI. It's relatively new, but it's again decentralized. There's a Dao and it's very interesting. So at Duke University, our blockchain club, we do many things, but one thing that we do that's particularly interesting is to vote. So we get delegated some governance tokens and the students study the proposals and then vote. And it's really a great learning experience. And our wallet is three out of five have to vote in favor of the proposal for our vote to count. So there's many different types of stablecoins. The third type of stablecoin is a purely algorithmic, one that doesn't have any collateral, that's much more challenging to do. It's got dynamic money supply and nowhere near as popular as the first two types of stablecoins.

Emmanuel
Okay, thank you very much. I think now it's time to pass to my colleague Raman.

Raman Uppal
Thank you very much for a very insightful and thought provoking talk. There are lots of questions in the chat, so I'm basically going to try and collect them and ask them by putting a few questions together each time. So the first one is about regulation and the question is, given the risks of DeFi, do we still need a regulator at all? And if we do, then is DeFi really very different from centralized finance? And if the regulator is very different from the current one, will we then have two parallel systems of regulation or will one take over the other one?

Campbell
So it's obviously challenging. In the US, the main regulation is the 1933 Securities Act and obviously doesn't mention cryptocurrency in 1933. So we don't have the laws to support this. And as I already mentioned, the regulators are just trying to figure out what's going on. So there's not much going on other than certain tokens have been identified as securities. And once you're a security, then you need to register at the SEC. It was interesting that the regulators went after Coinbase and I showed Coinbase as this very successful exchange. And what Coinbase was basically offering was you give us your US dollars and we will pay 4% interest on that.

Okay? So again, it just seemed like a great savings account. We know what Coinbase was going to do. They would take the dollars, transfer them into a stable coin like USDC, deploy into DeFi and make probably 8% and then have a profit for themselves and pay people 4%. That to me at the time was an obvious security. And if you're in this space, you need to know the basics of the most famous cases here. And the case that was relevant for this is Weaver versus Marine Bank in 1982. And that was a case where a certificate of deposit was challenged by a Marine Bank as being a security. And the court said it's not a security because Marine Bank is FDIC insured, so it's fully regulated, so they can offer this certificate of deposit. But the case also said that if something like this was issued by a non-regulated bank, then it would be a security. So basically the SEC delivered a Wells notice to Coinbase and Coinbase had to withdraw. I believe that they knew they had to withdraw, but they wanted to get the discussion started. And in decentralized finance, remember, Coinbase is a centralized exchange and it's relatively easy for the SEC to go after a centralized exchange because there is a CEO, there's a head office and board of directors.

But in decentralized finance, it's an algorithm. So how do you serve notice to an algorithm? And even if you took the extreme step of shutting down all of the nodes in the US where that algorithm resided, well, even if you did that the algorithm's running on thousands of computers around the world. So this is not just the US problem. It's not just a problem in France. So this is a global issue and it's extremely challenging. And then you might say, well, okay, we can't go after the algorithm. We'll go after the people that use the algorithm, the people that are putting the money in and getting their 6% interest rate. And that goes directly against the securities act of 1933.

That act was designed to protect the individuals using whatever protocol. So you can't go after the people you're trying to protect. This is very challenging, and I do think that certain things are kind of low hanging fruit for regulation. For example, the centralized stablecoins like USDC, that's just like a money market fund, it's almost identical. So they will be regulated and I think that USDC will welcome that regulation. But the decentralized stablecoins that are the competitors to the centralized is not obvious you can regulate them.

So they're more like a foreign currency that is pegged to the dollar. That's one way to think about that. Overall, this is really challenging. We probably need some regulatory guidance here, but it can only go so far. And people get around this. Again, you locate offshore, you don't want that. But that will happen. And I'm afraid, given the uncertainty right now, that many companies are locating offshore just because of the risk.

So what I think we should do is similar to what the CEO of Coinbase has said, that in the US right now we've got five different federal level regulators and each has a different view of cryptocurrency. So what we should do is to take all of that jurisdiction and focus on a new regulator that just focuses in this area and also don't do this quickly. Spend some time, take two or three years to get it right. And in the meantime, do a safe harbor. And the safe harbor means that nobody will be prosecuted for doing stuff over the next few years. I think it's really important that we encourage the innovation. And this is another issue that there's a lot of positives about this technology that the regulators already realize. And I remember when early in this space, Ben Bernanke was the Chair of the Federal Reserve and he was asked what he thought about cryptocurrency and he said, well, it's intriguing, and if it reduces transactions costs, then it's something that we need to take very seriously. So this is reducing transactions costs. So I think that this is not all bad. There's a lot of good here.

And the other thing that's important is the regulators have no love for the commercial banks. In the global financial crisis, these banks took excessive risk. And then when something happened that didn't seem like that big of a deal in the overall economy, given that their leverage was like 40 to one, that caused these banks to fail. And remember, how do they get that leverage? They're using the deposits that were insured by the regulator as collateral to borrow an excessive amount of money. The banks go under and then we need to bail them out.

And I've already mentioned that the banking system is highly concentrated. It's more concentrated in Europe than it is in the US. The regulators know this and the regulators are thinking, oh well, this is competition and that's a good thing. So give these commercial banks some competition. And of course there's another layer here, and that's the central banks. So this new space is basically competition for the central banks. Remember I said you've got many different tokens. One token might be linked to the euro, another token might be linked to gold. People can choose what they actually want to do. So money is kind of redefined and the central banks are scrambling. They're trying to launch the central bank digital currencies, or CBDCs, which are not cryptocurrencies, but they hope to make money transfer much more efficient and provide some competition to the cryptos. So that's a good thing, that there's more competition. However, I'm just not sure how appealing the central bank digital currency is going to be. So maybe this works in China, where the people in China are comfortable with the idea that the government sees every single expenditure. But in Europe and the US and Canada, I'm just not sure that's acceptable.

And it's also the case that the government can edit your account balance with the CBDC. So you've got some money, they decide to tax it away. So again, that is impossible to happen. In decentralized finance, the only person that can spend the money is the person with the private key. You cannot be expropriated. So I actually think it's kind of interesting what's happening with the central bank digital currency, but I also think it's too late that we're already at the point.

I did this analysis just the other day that if you look at the capitalization of the cryptocurrencies, it already equals the amount of notes and coins that are in the US. So I think it's too late.

Raman
It's too late. Emmanuel we have time for more questions or we are out of time.

Emmanuel
We can take a couple of two additional questions, and after we'll have to terminate.

Raman
Okay, so, Cam, the next question is about the small guy, like you know. We already have a lot of inequality even in the US. A very large segment of the population doesn't invest in the stock market. Cryptos are even more technically challenging. You know at least I could explain a saving account to my mother. But now to explain cryptos to my mother is much more difficult. So the question is in two parts. One, what should we do to help the small person who is not as well technically savvy? And two, what does it imply for inequality across the population?

Campbell
Okay, but this is exactly what crypto addresses. So let me give you an example of the country of Venezuela, which has got a hyperinflation of 700%. So if you're rich in Venezuela, this is annoying. It's not a disaster because you have a bank account in Florida, in Miami let's say, and it's in US dollars, so you're protected.

And the average person gets hammered by this hyperinflation, but no longer, because the average person has got a mobile phone. And on that mobile phone they can put some US dollar token. And there's plenty of apps that make this really easy to do.

So effectively, the rich are hedged because they've got US dollars, and the average person is hedged because they've got their own bank, which is their phone, and they have US dollars. And closer to home for you is the inflation that's happening in Turkey. So what are people doing?

Well, you've got 48% inflation. You're really worried that it's going to go even worse, so you dump your lira and buy US dollar token.

Okay, so think about what's happening here. Effectively, we're disintermediating the central banks, and effectively that Fiat inflation becomes less and less relevant. As for inequality, I've already said that this is a technology of inclusion. This is a technology of financial democracy. And these are not just spin words. So in this space, everybody is a peer. There's no banker client or retail investor, institutional investor. Everybody's equal, everybody's a peer. And a transaction can be large or it can be small, it doesn't matter.

And indeed, as I said, there are so many that are unbanked in this space. Everybody can be banked, so you can interact, you can participate in web 3. Even if you don't have a bank account. The current financial system, in my opinion, is making inequality worse. And this innovation of decentralized finance is actually addressing it. I think that it is true today. That the user interfaces are pretty clunky, it's true, but that is improving very rapidly and you can imagine that this will be something that would become easy and for anybody to actually use. I saw an academic paper the other day and they were trying to explain the phenomena. It's kind of what you said, the percentage of the population that has investment in stocks. And they showed a graph and basically it showed this pattern where if you're really young, you don't have much and then it increases and increases until, let's say you're 65 years old. So it's a positive slope. And it was using data from the US census, I think 2016.

And I said if we did that today, it's going to look the other way around because younger people have online accounts and they can go into a fintech and very easily, even with like €100 be in the market with a diversified portfolio buying fractions of shares. And then, oh, what about the tens of millions of people that have crypto wallets, like MetaMask or they have crypto in Robinhood or some fintech? You need to count those also. So if I'm investing in certain cryptos, it's like investing in stocks. Indeed. That exchange graph I showed you where it said not available in the US. That Exchange FTX was trading basically tokens that are collateralized by the stock. So essentially what's happening is that younger people and those below average in terms of the wealth distribution, they're all of a sudden in the market. So they've got these opportunities. And the last thing I'll say, and this is something that personally occurred to me, that before Facebook went public, a number of my wealthy friends were actively transacting in Facebook stock. It wasn't available to anybody else other than those that were highly qualified, which means they had very large portfolios. And this is something that basically perpetuates inequality. In this space the tokens are available to anybody, everybody is a peer.

So I believe fundamentally that this is a technology that has the potential to reduce inequality. But to take your question, I think it's a fair question right now we're not there yet, and that's very important in judging any technology that you need not to look at its limitations today, but look at where its potential lies. And you've seen this technology on a very steep vector. There's no reason that it won't continue to be steep. And we will see changes and improvements to make this much more accessible to anybody. So it'll be no different. You go to the grocery store and you buy some groceries and today you might use your mobile phone and tap it, but tomorrow you'll have the same technology, but you tap what you want to use to pay. So maybe you want to pay in gold, maybe you want to pay an Apple stock, no problem to do it and it'll be seamless. And that's just the way it's going. And again, this is not different from any other technological revolution. The Internet is a great example. I was using the Internet for email and file transfer in 1981 and that was very clunky, very difficult to use. And it wasn't until the early 1990s that we got the World Wide Web that made it easy. It's a good question.

Raman
Thank you, Cam. All yours, Emmanuel.

Emmanuel
Okay, thank you very much. So. Thank you, Raman. Thank you, Romain, and thank you so much, Cam. I think we could spend 1 hour more you know just to discuss it's. So interesting topic.

What, I just don't know that we have to read the book, we have to invest, especially as finance professor, but also, as I would say, students and citizens. And so really a big thank you. Thank you, Cam. As a reminder, in fact, the presentation, the slides and the video of the recording in fact will be posted on our dedicated web page. I have something which is personal. I already said that to come into Raman. This is a special session, at least for me, 20 years ago, I was a very young PhD student and I think this was one of my first conference was presenting in England at inquire Europe and I was presenting in front of two guys, Cam and Raman. And I remember this time very vividly. So yes, thank you so much. Thank you Cam. And we know what we have to do now. So thanks for the time, thanks for your expertise and thanks to all. See you. For the next session, we will continue the journey on the future of finance. We will have, in fact, a presentation from the Quant head of Research from Rubeco that will talk about the application of AI and ML in Quant investing.

Thank you. See you. Bye bye. Thank you, Cam.

Campbell
Thank you.

Emmanuel
Thank you, Raman. And thank you, Romain.

Make an impact
EDHEC BUSINESS SCHOOL
[Music]

 

[Music]
EDHEC Business School
EDHEC Speaker Series - The future of Finance
Will private markets kill them all?

Ludivic Phalippou
Professor of Financial economics, Head of Finance,
Accounting and management economics
Academic area at University of Oxford Said
Business School

Thursday, 24 March 2022
6 P.M - 7 P.M Paris time

Emmanuel Jurczenko
Okay, so let's go, let's go ahead. So, hello everyone. Welcome to our new EDHEC Virtual Speaker series session on the Future of Finance. So, I'm Emmanuel Jurczenko. I'm the director of the Graduate Finance program here at EDHEC. And it's my pleasure in fact, to welcome you today. So, as usual, you can use the Q&A chat box, in fact, to ask questions. We will pass that to Ludo after his presentation and we will post also the slides and the recorded video will be on our dedicated web page. Okay, so, all right, let's move to the program. So, first I want to introduce our two co-moderators. First, I'm pleased today to have Hamid Boustanifar, which is Corporate Finance Expert at EDHEC. And I'm also pleased, in fact, to have Mathys Lopez. So Mathys Lopez is a student from our Corporate Finance MSc program. And it's my great pleasure to have today professor Ludovic Phalippou. So, Ludo will be able, in fact, to I think there is a slide in your presentation where you will be able to introduce yourself better than I. But I just want to mention that Ludo is an expert in private equity.

It's more than 20 years that he work on private equity, private markets. His research is regularly, extensively sitting in the press in academia, thanks to Ludo. Now, we know how to use interval rate of return. We know a lot of stuff with respect to how not to be manipulated with respect to performance in private markets. What is important is to know that Ludo, beside all his research, is also wine enthusiast and is a black addict. So, Ludo is French. Nobody's perfect. So, Ludo, we are very fortunate to have you. Really, really a big thank you. And now the floor is yours. Thank you, Ludo. Thank you for your time and for sharing your expertise on the private markets question. Will private markets kill us all? We will see that.

Thank you, Ludo.

Ludovic Phalippou
Thank you, Emmanuel, for having me. So, let me share the slides, which I prepared just to tell you that the title of a talk is not going to be the content of a talk. We flip flopped about it. And so I decided to talk about a recent research project that was very applied and probably more interesting for the audience here. But I'm happy to answer any questions about private equity. Like Emmanuel said, I found nothing better to do with my life over the last 22 years than doing research on private equity. That's all I've done. I've been eating private equity for breakfast, lunch and dinner every single day of the year. So if you have any questions, feel free. Doesn't really matter, but that's me. You can also follow me on social media. I have a website with all kinds of resources. I have a book, I have a podcast series, et cetera, et cetera.

So basically, when we talk about private equity, private markets, et cetera. What we're talking about are these funds that intermediate money privately. So these private market funds are usually set up as limited partnerships and well-known managers are firms like Blackstone, KKR, Carlyle, Permira and so on and so forth.

And over the years, they have accumulated a lot of money. So when I started you know, working on private equities, the beginning of this chart on the left hand side, the data starts at that date because basically at the time there was hardly any data. And I started working on this topic because I was one of the first academics to see private equity data. And already at the time we were saying, oh, you should pay attention to priority because it's quite big, it's nearly 1 trillion. Okay? So we were saying it's a big deal. And then if you look at the evolution of the amount of money managed by these funds, it just went exponentially, right? So it's still right above 1 trillion. Like in 2004 when I graduate, it jumps a bit, 2005, 6 and 7. And then we're saying, oh my God and everybody was talking about private equity and my research on private equity became very circulated quite a lot because there was so much money going into private equity in five or six or seven. I was doing research on other things, but that was then getting a lot of attention because they were booming these markets.

But yet we were just barely at 2 trillion in 2006. Then during a financial crisis, it just stays flat, basically. And after that is using skyrockets, continues to grow very fast until 16 and then 17, 18, 19, just like goes through the roof. And the last two bars, they are close to one over, just because one is March and 2020 and the other one was December 2019. I didn't bother update this slide, but if I had put it to December 2021, you see it's 8 trillion in 2019, in 2021, December. So the last data we have is at over $10 trillion. So imagine a borrower 10 trillion and you will see how exponential it has been, especially over the last three, four years. So certainly now everybody is aware that private markets and private equity is a big deal and there's lots of money in there and they influence quite a lot of companies and lots of things. To give you an idea of what 10 trillion is. So, you know, Euronext, which is not just Paris, it's Paris, Amsterdam and a few others, Euronext all together is 5 trillion. The London Stock Exchange is a bit less than 4 and the Deutsche Borse German stock market is 2 trillion.

So basically you would need to add up basically all the stock exchanges in Europe and you will have a size of world private markets funds, AUM. So it's pretty big deal. So I'm not going to go through that graph so much because as I said, I'm going to focus on one research project and I want to give time for Q&A's because I think it will be more interesting. But I'm happy to go back to there. This is what makes priority different from the normal setup in terms of governance of companies and also about the fee deals they have with investors and so on. So there are some things that are unique about private equity and that generates a number of big questions like the money of private equity firms comes from someone else is other people's money, but these people are in control. And so usually in finance we are quite worried when somebody is in control with other people's money because there is all kinds of conflicts of interest which we study and document in corporate finance. Other big questions out there is like what is the pros and cons of having a privately managed company versus a publicly listed one?

Since there is less and less publicly listed companies and more private equity ones, we worry a lot about stakeholders in prioriti context, we worry a lot about employees and the like. And the reason for that is that prioritically firms are very, very profit focused and we are a bit afraid that when somebody is very profit focused, they may not care too much about the externalities that they have on employees, environment and so on. And we are also interested what I was in LP's payment for this financial intermediation, how expensive it is and how much return do they get, how much volatility is out there, et cetera. And debunking a few myths around the risk return profile of private equity. I have written this book which is doing okay. It's available on Amazon. It has the feature of being very cheap compared to other private equity books. So I think in France it's at €25 if not less and it's even available on hard copy and it covers everything about private equity. It covers the fund sides of things, it covers these questions about the impact on society and impact investing, et cetera. And it covers all the issues about limited partners and the fee structures and so on and so forth.

So this project I wanted to talk to you about is about trying to find something to replicate for ethically returns in the public market. So I've been working on that for 15 years. Lots of banks have always been very interested in developing such products and I've worked with a number of banks over the years trying to do that and we never really found good traction. It was a very hard sale. But this one project here, which to be transparent, was developed with J.P. Morgan an I helped developing this product and I think it's quite innovative and pretty cool. But we may decline it in different versions with some other people. But this product is actually already live on Bloomberg for a year and we developed it three years ago. There was a two years embargo on the research and so now the paper is available. So why would we want to have something that replicates private equity returns and we are focusing on buyout returns here. So we want that because sometime institutional investors want to have a readily accessible solution to hedge. So for example, if they are afraid that they are overlocated to private equity and a bit too exposed to private equity, they want something that they could quickly short.

For example, to reduce their exposure to private equity, vice versa. If they are under investing in private equity and they want to quickly get to their optimal location, they may want to use a quick solution. So this idea of quickly altering exposures and also currently because people are never you need to commit and then people call only the money over time. There is this strategy, it's called overcommitting. You basically say I want to commit 200 to private equity when you want to invest only 100. And that presents some risk because you said I want to spend 200 million on this. But what you want is to have at any point in time 100 million. But it could happen that you suddenly get 200 million deployed in there and then the rest of your holdings go down in value and you are very overexposed to private equity. So the institutional investors may want these kind of products and also high net worth individuals may find that it is easier to access private equity via a public listed vehicle because then you avoid the high fees, it's more transparent and so on and so forth. But I can tell you a bit about the existing solutions but what is most interesting is how we go about it rather than why people may be interested.

I think it's interesting per se what we did. And the reason this is interesting that's why I thought it would be cool for students like in a master level, is that this is not just for private equity but it's index in general. So there are all kinds of indices out there. There's actually more indices than there are stocks. So you will have indices on like the leisure industry. You would have an index on green transition. You will have indices on absolutely everything. And the way people build these indices is that they have some analysts that looks at all these stocks one by one and say is this like green transition yes or no? And then say okay, this one is a yes, this one is a no. So it's a binary decision and once they have all the stocks that they say okay, these are like green transition stocks then they say okay, then we take the market cap of the stock and then this is what we use as a weight and then that's it. That's our index. It's a very adult way of coming up with weights of different companies, right? It's a binary decision.

Zero, one, and so if you have a big company that was marginally green transition and you decided it was green transition and then it's a big company, then suddenly it's in this index with a massive weight because it's a large company and it was even marginal decision whether it should be included or not in green transition. So it's how all the indices are constructed and it's a bit weird and unintuitive, really. Some other people also try to do factor analysis, so they say, okay, so it is green transition, it loads on this and that factor. So I'm going to make an index with different factors and that would give me the exposure. The bottom line is it's not so interesting, what others are doing is not very good so here is what I think is a cool stuff, and let me tell you then about what we did. The idea is that instead of saying this company is a yes or a no for green transition of a private equity, whatever you want to do, the idea is to say how much is this company exposed to the thing I want to capture? So imagine I would like to capture private equity firms that care about ESG.

Then the idea would be different people will have different degrees of how much they care. So Emmanuel may care a little bit more than me and Enrique may care zero, right? So we would have different degrees of caring about that. Then the question is, how do you find out how much Emmanuel cares about ESG and then how much private equity does in life? So you could say, well, I'm going to go on the website of Emmanuel and the one of Ludo, et cetera, and I'm going to look for what they say on the website. Well, maybe I have a very small website, Emmanuel has a big website and so the words private equity will be there ten times on Emmanuel website because he has a big one. And I have just a small website, I hardly write anything and so I'm not there. You could look in disclosures of companies, et cetera. It has all kinds of limitations. We felt that the most accurate way available to know whether somebody is doing something is to study press release. Because nowadays any company, whatever they do, they just issue a press release. So if I decide to invest in whatever, I put a press release.

If I acquire the company doing whatever, I put a press release. If I'm celebrating my earnings and that I have acquired some new clients, I will issue a press release and so on and so forth. And we have software and databases of all the press releases in the world. So it started basically in 2006 to numerousize all the press release. And by now I think we are like 10 million press release. And so you have all these 10 million press release, about 30,000 different companies that are publicly listed and then you can study whether people what is it they say. If somebody is always talking about private equity in the press release, it's probably that they are doing quite a lot of private equity. If somebody is saying in one in ten that they are doing private equity, well, they're not doing that much private equity, right? So think about partners group in Switzerland. In basically nearly every single press release they will talk about private equity because that's all they do. If you think about the Blackstone, they're not actually going to talk about private equity in all their press release because they do a lot of real estate, hedge funds, all kinds of other things.

And so Blackstone will talk about private equity in about half, two thirds of a press release, but not all of them. And if you take a BlackRock, BlackRock does quite a large amount, has quite a large amount of money in private equity, they have like 30 billion, but it's a 7 trillion company. Private equity is a small part of BlackRock. So if you look at BlackRock press release, private equity is there often because they do quite a lot. But out of all the press release of BlackRock, 10% is private equity. And so that gives you how much a company is exposed to a given industry, how much of it do they do of something. And you can add like an ESG component by saying I want both private equity and ESG and so on and so forth.

So basically, in a nutshell, we wanted a portfolio of stocks that captures private equity. And the way we did that then is like I described, this is some more reasons for why over waste are pretty poor. And we went for analyzing the text of news of companies and we searched for words like leverage buyout and private equity. So the advantage of having a portfolio of stocks that are capturing private equity or that do private equity is that it is cost-effective. So if you are just trading a portfolio of stocks, it's usually pretty cheap and that's very important. Quickly add new entrants and people exiting, et cetera. So, for example, there has been a lot of mergers recently or M&A activities whereby a financial institution that was not doing any private equity has suddenly bought a company. So, for example, Landmark, which does only like secondary private equity, was bought by a bigger company that was not doing any private equity. And they bought Landmark because they wanted to have a private equity division. So that company that didn't have any private equity suddenly now they're going to be doing quite a bit of private equity and there's going to be press release with the word private equity with their name and our index is going to pick that up immediately. You don't need to wait for the next meeting of analysts to go through all the MNAs and say, oh yeah, this company, yeah, they used not to be private equity but now they bought Landmark and Landmark is pretty big. Let me check how big Landmark is in terms of revenue share to this company. Or maybe should I use revenue share or maybe I should use profit shares.

And you go on and on and then you're late, you don't know what to do anyway. And with press releases then it's easier. As soon as these guys start buying PE businesses, then they show up in private equity related news and your index picks that up. So it's actually quite precise. They're also very flexible because if you wanted to capture some Chinese companies, you could add like a Chinese name for private equity. You can add words like venture capital in case you don't want just private equity leveraged buyout. But you're also happy with venture capital, et cetera. And we had also all kinds of innovations on the way we dynamically trade this portfolio so that it's liquidity efficient and I'm happy to explain that if you're interested. So this is the news articles, what I was mentioning. The company is called Ravenpak and they have digitalized these articles, but they have done more than digitalizing articles. What they've done, which is really cool, is that they have tagged everything and they tell you where these things are in the article. So for example, imagine that Emmanuel is showing up in a number of articles that are private equity, but he's always in like paragraph four or five in the press release.

So probably Emmanuel is an advisor or like someone who's vaguely related to that news. And so then we're not going to put a lot of weight on Emmanuel being in the news about private equity because he was very low in the article where I mentioned him. But if he's mentioned in the title of a press release and we have Emmanuel and private equity in the title, then it's big deal. Like Emmanuel is really doing, he's there in the title with the name private equity. So Ravenpack would have this weight as a function of where someone is mentioned in an article in relation to the keywords we are looking after. So this is really, really powerful. And again, you can do that with all kinds of things. You can do it with climate change, you can do it with anything you want.

So there are things to be careful about and how to optimize with portfolio constraints, et cetera. It's actually a very cool mathematical problem. And we ended up with this index and we didn't even constrain the weights or anything and we ended up with lots of names, everybody with a reasonable weight, again, even though we didn't put a cap on weights.

And the ranking is making sense. You see, for example, KKR has a lower market cap than Blackstone in the standard index. Blackstone has a higher weight than KKR because Blackstone has a bigger market cap. But here KKR has more weight. Why is that? Because KKR does almost only private equity while Blackstone does all kinds of other things. And this is how you end up with this weight that actually makes complete sense. And there were some names that at first I was surprised by, like, for example, Houlihan Lokey, which is like an investment bank. And I was like, Is that a mistake? Why is this investment bank showing up in my number 16? And then you read about that and then you realize that this Houlihan Lokey is actually a placement agent. They advise on PE restructuring and sell side advisory work for mid cap companies. So it is an investment bank, but this investment bank is doing only private equity. And this is how in a normal index, you won't find an investment bank in the index. But you should have an investment bank like this one, because all it does is private equity. Sunny is a fund administrator in Luxembourg, same thing.

They only do private equity and that software picked it up. I wouldn't have picked it up by hand. And I think not a single private equity listed index has it in its index and the index did extremely well. That was not the goal, but the performance are extremely high the last year, actually it's up 80%. It has been extremely strong. And because we are more diversified as well, because we go to add investment banks, fund administrators and the like, we have lower volatility. So I just wanted to give you a sense of all these topics. I zoomed in a bit on a recent research project just so that you can get a sense of one particular project I was working on. But like I said at the beginning, do take advantage to ask any questions that you're interested in and let's do that as a Q&A. But I believe first it goes to Hamid right, Emmanuel?

Emmanuel
Yeah. So thank you very much, Ludo. So now I will let Hamid, followed by Mathys and as usual said for all the participants, do not hesitate to send questions in the Q&A so that we can pass it to Ludo. Hamid.

Hamid Boustanifar
OK, thank you very much Ludo, for this great presentation and showing us the power of textual analysis. In fact, I am a big fan of these types of work and I wish actually I had the pleasure of reading this kind of simple but very powerful and sort of applied work more within finance. So anyways, today I had the pleasure of reading this. I was not aware of this paper, although it's been around at least for some time apparently, and it turns out that I have been doing independently very similar stuff, which I thought actually now to show some of the things I have done also with a different data set. And then from that I try to ask a few questions to see how we can kind of merge these things together in a way. So I am not a presenter, but I can try to actually share my screen and let me know if you see graph.

Emmanuel
Yeah, we can see it.

Hamid
So, some time ago I was thinking about the same sort of idea that how can we look at, can we come up with a sort of an index that picks up the private equity and developments of it super simple. Instead of going to press releases, I went to the annual filings in the UK, in the US. And I just get the count of private equity and private markets. And I tried actually venture capital also, but it doesn't change big things. So I'm just showing you here private equity, private markets. And here I just did a simple on the blue line, I did a simple count, the average counts that I saw since this is the data since mid 90s where the publicly listed companies, they were filing electronically. And you see also the increase in here and the red lines showing you the number of the firms that this shows up as reporting at least more than ten counts of private equity, private markets in their filings. And then I tried to do a similar sort of analysis by looking at what happens, what are the top companies in the list that shows up. So here's in 2020, of course, each year could change a little bit.

And I see some usual names in here, see this KKR showing up relatively high and some bunch of other names in here. And then I see also exactly 2020, there's one new name because if I look at 2019, this is the 2019 data, this Step Stone is not there 2020, it's there seems that entrance we have in the market. So this also seems to be quite reactive in a way what Ludo was saying. The question is that how can we compare these two sort of analysis that we get, this sort of results that we get, which ones it's more reliable. So one thing I was thinking about the press release, it's quite nice because it's not something that the company itself is revealing. It's like unlike the annual filings. So that's why I think if you want to look at the things like let's say social, corporate social responsibility, you have no hope of going to the company's filings because everyone is good. So everyone is going to say that I am good, I don't plut, I don't do this and that. But going to the press release, it's maybe much more reliable. When you go to the business of the firms, let's say, what kind of business overall they are involved in.

I just want to raise this question that I am not 100% sure. For example, going to the press release necessarily is the best idea because at least one could think that if I am involved in, let's say private equity size but is more I do some more controversial things that people don't like and get a lot of press coverage, then I am going to show up a lot on the private equity side. But it's just more controversial aspect of me. So is that something that Ludo you are concerned about that the news may not be reflecting really the business of the firm and more focused and biased towards the more, let's say controversial aspects. I don't know whether in private equity per se it's going to be a big deal, but at least one could think of it in other dimensions that could be biased right towards specific actions.

Ludovic
Yeah, I think that there are some industries or keywords that wouldn't work. So I think one needs to be very careful about that. So in private equity and buyout, you can see it from the firms that are being generated. That's really not an issue. There is no one out there that spends their life criticizing private equity but does not do any. So you could think an example, the best example of what you would say is, like, Warren Buffett is usually not very impressed with private equity. And so you could have Warren Buffett in the news always with this Berkshire Hathaway saying, oh, private equity. I saw cheating and it's all terrible, et cetera, and I would never do that. But the odds, that one press release in three or one in two from Berkshire Hathaway would be about him saying I will never do private equity is pretty small. So if you would pick up, people would just have like one or 2% of a press release in private equity. But here the filter we have even is 25% of one third. So the chances here are very little, but it depends on the sectors and what you want to capture.

So recently someone was looking at can we do this for reduced indices? Where you look at scenes and things like that. If you list the scenes it will be quite difficult to program to make sure you take the people that do not sing versus the people who spend their time singing because the words will be there a lot in the press release. So either it requires more advanced language to find the negatives and things like that around the world or I think it's safer to say that there are just some sectors that are more difficult to capture. So if you wanted to capture, for example, real estate and said I want an alternative to the REITs, I don't want to go with REITs, I just want to know people involved in real estate. If you use real estate you can have all kinds of reasons. Somebody mentions real estate repeatedly and press release, not necessarily because they are in the business of real estate. So real estate would be an example of something very difficult. Another one that is even more difficult is infrastructure because people use that word for different contexts. And so if you want to capture infrastructure, investing that way, that would be very tricky.

I think we've leveraged. Buyout and private equity you're in safe hand. And then when you see the firms that are generated by the algorithm, it makes complete sense. Like like I said, there are some firms that you don't know and then you check what they do and the advantage that you can always check, right? So you have these firms you don't expect and then you get the press release that mentions the words and you can see whether this press release are indeed things they have done attained the business or not. It's working well.

Hamid
Exactly. So that's the first kind of filter that out of 40,000 companies gives you like 50 companies. Then you can take a look and see which.

Ludovic
Because if you do the 10k like you were saying, you have some people who describe imagine you have BlackRock. I mean, BlackRock doesn't write it this way, but BlackRock could very well spend a lot of part in their 10k filings explaining, at the risk of business, that the big risk of their business is that private equity grows, and then the ETFs are dying because there is not enough publicly listed firms. And so private equity is a big threat to them and so on and so forth. And so they explain that private equity is a threat and so on and then you say Oh My God, like BlackRock is mentioning private equity all over the place but if you buy that stock is the opposite. They say they are threatened by private equity in the press release. It's not going to happen. Somebody's not going to issue press releases to say I'm threatened by X and if they issue press release, say what they do.

Hamid
Thank you very much. So I hand it over to Mathys to continue. Maybe we come back again with some questions later on. Thank you very much.

Mathys Lopez
Okay, thank you, Hamid. Thank you Ludo, for the presentation. I have a question regarding the algorithm also what I mean and what I want to ask, if companies decide to increase their media coverage with press release, how will the algorithm react to them? What I mean is that how will you differentiate good companies in term of press release and good companies in term of returns?

Ludovic
So we're not after returns, we just want to know who's in the business of private equity. So we're not trying to filter out the winners or the losers or things like that about the companies increasing media coverage or media presence. We take the fraction of articles that are mentioning private equity out of all articles that are about them. This is how you have a BlackRock has in absolute numbers lots of articles that mention private equity in BlackRock, but in proportion of all their articles it's peanuts. So they're not in. So it's all proportional. So if BlackRock just decides to increase media coverage, you can only be toast if you have a firm that says we're going to increase media coverage but only to talk about our private equity for no reason. Like business wise, it doesn't make any difference. We're just going to talk more about our private equity division. Why would you do that if it's not to boost your private equity business and because you want to expand it or like, yeah, why would you do that? So the total number of articles that doesn't influence the score.

Mathys
Okay, understood. I have a more general question regarding return computation in private equity. So we know that there is some flows. By using IR, do you believe that LPs will try to convince GPS to have a new method? For example, we know about modified IR in Excel. Do you have any thoughts about that?

Ludovic
So I've been saying this for 20 years and nobody has ever changed anything. I know by now that nothing will change unless the SCC imposes it or something like that. But I don't think it will change.

Emmanuel
Perhaps I think if I may jump, obviously this is something which is super interesting, because you don't have only dumb people, right? In this industry. You have a lot of smart people. And Ludo effectively, is the one that really makes sure that people understand, in fact, how you can game those metrics. So it's still a bit to see that big investor that mean at least to multiples, right? Which would give you a better so.

Ludovic
The smarter one, they may mention multiples, but if you take one is to not forget that people are conflicted. So if you take probably the guy who was the most knowledgeable about private equity is David Swensen. You take David Swensen, you take the annual report of Yale endowment. It was only IR being reported. And so after a while and me putting the pressure on this, saying Yale is a hero because of the number that is in the annual report every year, but it's always the same number, and that's because it's an IR and it's a completely stupid number. And then after a lot of pressure, he confessed that indeed it was an IR. And then he said, but we use other metrics. We use also multiple and PMEs and all these things. And the question was, why don't you put that in your annual report then? You're using them. You say you acknowledge that this is a flood metric and there are some better metrics, and you are using them. You're just telling me you're using them, why they're not in your annual report. And the reason is because they're not as impressive.

A 30% return is outstanding, say Yale has done 30% in practically over 30 years. Seriously, it's amazing. That's impressive. If you say, what is the multiple of Yale? If I had to guess, probably 1.5, 1.6 times the money. Is that impressive? Well, that's what the stock market gives you over a five years period. So it probably is good, but it's not something to write home about. A 30% return is something to write home about over a long time period.

Emmanuel
People like to dream.

Ludovic
Yeah, but you also have David Swensen was paid $5 million a year, right, because he was a hero and because he had this reputation. You should not forget all the different layers of conflicts that people are facing. The private equity division of CalPERS, they are happy to report a good IR because the board doesn't get it anyway, so they know. But it's cool to have a measure that exaggerates things because they can go to the board and see we are geniuses, we need to hire more people and to be paid more. We have a bit this issue with people are saying, oh, they are sophisticated, so it must be good. So you must be wrong because these people are smart. So it cannot be bad as people forget the multilayers of conflicts of interest that people are facing. So that's the main reason. The other reason also is that we don't have a good replacement. So even though modified IR is imperfect and so the truth is that we do not have a perfect measure to replace IR. We may devise it. It's something I'm working a bit on now because I've been pushed hard for many years.

I just said just forget about the rate of return, just use a PME, use an NPV like you're told in corporate finance, okay? Just use net present value, don't use IR. And with an NPV you have something to be careful about, but you'd be okay. But I think that people cannot live without a rate of return. They are used to it so much. They want to know annualized what are we talking about? If I say you have an NPV of 1 million, I don't know what that means. I don't know if it's a lot, I don't know if it's not a lot. Tell me. Percentage per year. And the problem is that annualized returns, rate of returns are defined only for continuously traded assets. And so then in private equity they're just not defined. So we don't have a solution. And so IR is a stupid solution, but we don't have a perfect solution because there is no solution. So there is one that I'm thinking about, and I may propose, but I'm quite pessimistic about the odds of people adopting it. The bottom line is that it is impossible to talk about rates of returns for something that's not publicly continuously traded. That's it.

But people are so used to it and it's because of the stock market, because I'm pretty sure that if we go back to archives before the stock market, when people were talking about investing, they were talking in multiples and number of years, I'm sure they said with this investment you double your money in eight years. I'm sure this is how they were thinking about it. And it's only because of the stock market that people have got into their mind that it is all about? Is it a 4% or 6% investment a year return a year? It's not possible to think that way. I cannot calculate a rate of return for a house. I cannot calculate a rate of return for any private equity. Not without serious assumptions. And so IR makes assumptions and they are very stupid. But we don't have an ideal alternative. That's why it sticks.

Emmanuel
Thanks, Mathys. I think you have other questions?

Mathys
Yes, maybe a last one before the Q&A. Regarding the news this time we know that the Court de Cassation is trying to impose higher taxes on carried interest. I've read that you talk about the lack of regulation and taxes in the industry. What is your guess about this new law? How pay funds will react to it?

Ludovic
So I've always said very clearly that the taxation of carried interest as a capital gain was the biggest BS I have ever seen. This is absolutely shocking. I don't care about people how much taxes they pay. Okay? Some people may say people should pay low taxes or high taxes. I don't care. That's not my point. I have a personal opinion, but that's not as an academic, I don't need and I shouldn't have an opinion about that. But telling people and convincing the regulators or whatever of the government that carried interest is a capital gain. It's BS to the power ten like this is absolutely incredible. Basically, a carried interest is a bonus for having performed in a certain way. And a bonus if you're working in a company and you get a bonus, you pay income taxes on it. So you can debate that it should be the same tax rate for income taxes or for capital gain taxes. You can tell me that you think all the taxes should be at 10%. I won't have anything to say against that, but to tell me that if you have a bonus for good performance in a company, you would pay income tax rates on this i.e 45, 50%.

But if you are in, theoretically you structure it the way they do, and then magically it becomes a capital gain and they should be taxed at 15% to 20%, depending on countries. It's absolutely atrocious. So I'm happy to go into the details of why it isn't a capital gain. It is totally a bonus for performance and how the industry is managing to play on words and to phrase things in a certain way, structure things in a certain way to say, oh, I did put money into that business and I just happened to receive this and that's related to the money I put into the company. So I should be taxed 15%. It really is to the same extent as if I'm receiving a fixed salary from side business school. And every year, which I actually do, I would make a donation to the school. I would say I put 10,000 onto the school, into the Executive Education arm of a school. And at the end of the year, as a function of how much profit there has been in Executive Education, I would receive something like a million dollars a year. And I would say, see, this is a capital gain because I gave 10,000 into Executive Education at the beginning of the year and I'm receiving 1 million. So that's a capital gain. Like, no, you put 10,000, your 10,000 were not at, your 10,000 are not what converted into 1 million. Your 10,000 were at risk, but not for the 1 million. So that cannot be a capital gain.

So it's a tricky issue. It would take me a fair half an hour to show exactly why and things are phrased the way they are phrased and why it led the tax offices to think and treat it as a capital gain. Because obviously if it has been lasting for 30 years in all the countries in the world, et cetera, is that the trick is pretty subtle in the way they phrase it and so on. They are clever private equity guys, okay, so they know how to phrase things. But this is purely bullshit. It's absolutely incredible. And again, I could do that myself. I could very well tell side business school, you pay me only $50,000 a year as a salary. I put 10,000 of my own money out of this 50,000 into Executive Education, and then I get a share of the profits of ExecEd. And so in some years he may get 1 million, some years 2 million, and that will be a capital gain.

I could totally structure it that way my pay package, but I would get into trouble if I was doing that. Private equity get away with it. And it's exactly structure of the way I described. So it's totally unfair. So that's one thing about their taxation and then your question you ask is what would happen if we tax them properly? i.e at income tax levels. Well, they could just going to live somewhere else. They're just going to go and live somewhere else. Doesn't change anything. They will live in Monaco, in Andora. It's a perfectly nice place to live in. It's fine. The rent is a bit higher for some of these places, a bit more boring than to live in Paris or London, but they may go into Switzerland. At least they can cycle and ski, although in Andora as well, I'm a big fan of Andora.

Hamid
They charge high fees and then they pay less on taxes on that side. And what do we expect on Econ 101? That there's a lot of entry into this market, right? But are there a lot of barriers?

Ludovic
You want to witness entry, you want to witness desire to entry? I can invite you to my MBA students at Oxford and you should do raise of hand. All of you wants a career in Finance. How many of you wants to work for a hedge fund, you may get one hand up, okay, if you're lucky, or how many of you wants to work on ETFs and factory investing and all these things you guys love at EDHEC, maybe you'll get one hand up. And sure, ask them how many of them wants to do trading of bonds and derivatives and so on. You won't get a hand up. Ask them how many of them wants to work in private equity and everybody not only one hand up, they probably raise both hands and their feet and tell you, this is where we want to go. So you want to want to see desire to entry? No problem. I can show you desire to entry. And you do. And I have like 10 teams of students a year who try to raise their own funds, okay? There is no problem to convert from a desire to going on the road to raise money. There's no problem with that.

The issue is that because private equity is such a wild deal for the investors, that the only thing that investors can do is like, look, I need to invest with IBM because I won't get fired for investing in IBM. But if Emmanuel raises a fund tomorrow and I invest with him, when you invest in private equity, you give the keys off your car and off your house to a private equity guy. You just have to pray that this is going to be fine and they're not going to crash your car and destroy your house. You just give your keys and that's it. They can do anything they want. So it's a lot about trust. So if it's a big name and somebody who has done a lot of that in the past, then you're cool about it. But if Emmanuel is raising a fund tomorrow, it's very tough because it's hard to trust Emmanuel. He has no experience in private equity so desire to entry plenty of people, people trying to raise funds, tons of them, people managing to raise a fund, that's another story. But that's because of the structure of private equity, because you have no control, you have no regulation to protect you as an investor, nothing.

So all you have is a name. You invest in a name and the name is unique, right? There's no entry. It's a bit like schools, right? You could say, imagine that business schools will make money, which they don't imagine they do. And you say, well, there is no new entry. There are plenty of entries, but the ranking is where it is. So if there is like only top ten business schools that make money because everybody wants to have a degree from these schools, you can have as many entry as you want. It's the name. So private equity is the name.

Emmanuel
Is there a capacity issue for private equity funds? Because this is what you're saying, that you have the big names, but the bigger they are in term of performance.

Ludovic
Yeah, they are growing pretty fast. I mean Blackstone by now is half a trillion. So one could say, okay, well, so if you go by your name and then Blackstone should it's very interesting your reasoning. It's very good thinking. You would say, okay, so if only the names matter. So you have only 20 firms people want to invest into, let's say then these firms should grow exponentially. But I think they do, right? So you could say, well, why don't they grow even faster? Well, they're already like pretty punchy. So of know, you need to add teams and so on. But they're doing that. Could they do it faster? Yeah, but there is business risk at growing too fast as well. So they are growing at a fairly intense space. Look at Pounds group in Switzerland. These guys are growing at speed of light. Ardian in Paris, speed of light. It's pretty hard to think like okay, you could go even faster. Really? So I think that's max.

Hamid
Have you thought of raising money Ludo?

Ludovic
Yeah. So for full transparency there is a nice case study. So I was having a conversation once with like a senior guy at McKinsey and he was asking me about what McKinsey should do, what would I do if I was running McKinsey? And I said, well, what you should do and in spirit of all my research and the like is you should kill the intermediaries because basically, private equity firms are getting the money from the pension funds and then they hire you to fix the companies and they are pocketing like all these fees. You should go to the companies directly and you should raise the money from the pension funds to take control of these companies. And you do the job. You don't need all these guys to be in between you and the pension fund. Just go to pension funds directly, raise money, get companies with the pension funds money, you fix them and you avoid all these layers of fees and you contract directly with the pension fund. That's what you should do. That's what I would do as a CEO of McKinsey. And the guy was like shit, this is good, this is very good idea.

Then the guy decided he took all his team out of McKinsey and they were like 30 years McKinsey people. So like seriously well off people, right? They had a very comfortable life. Maybe they could have spent another ten at McKinsey. So they came out of McKinsey and they started their own fund and they started like I told them, I said go directly to some big asset owners and say to them that you want to be their direct private equity investing arms and you will do the sorts of things you are doing in McKinsey. So if you are specializing in tech and McKinsey, you go to these family offices and you say you give me the money, I will do exactly what I do in McKinsey, I will get companies for you, and you will be my only two investors. So you have full transparency on me. I'm like your employee outsourced. So they got in no time. This guy gave a couple of phone calls. He got two families giving half a billion dollars each. So he raised like $1 billion in five phone calls, like exactly telling him what my line was. So then, he called me and he said, maybe you should be involved with our company.

We are actually starting it, so I'll be a bit involved with them, but there's just so many things I can do, and I'm not purely maximizing wealth, so I'd be involved. I'm interested in that. I think that's the right thing to do. But me raising money and investing in companies and so on, that's not my job. I'm an actor, I'm a speaker.

Hamid
If you are involved in there, what are you going to report instead of IRR?

Ludovic
Well, since they work with these families, yeah, but they work with these families directly. They get these businesses and say, okay, this is how much money you invested, this is how much we return to you. You don't need to do any trick. They're not going to do any credit line in order to pump up an IRR or anything like that. If you have only a few investments, like they do here, ten investments, these families will very well see how much money was put in by them, how much money was taken out, and then what is the value of the current investments? Just ten investments. These families were involved with the decision of investing in these things. Sometimes a family helps you with the sourcing, et cetera. You don't have any trick to play them or whatever. And in terms of fees, they are your shareholder, so you have no conflicts of interest. They own you, so then whatever, you're not going to charge hidden fees or whatever. They own your company, so you're effectively their employees.

Emmanuel
On my side, there is something I wanted to discuss, Ludo, with going back to the application of having a liquid kind of mimicking portfolio for private equity. There is something, I guess, which is also important for investors, is that when you're investing in something which is illiquid, prices are not moving, so you don't see volatility,...

Ludovic
You will not have his benefits. But I always tell people if what you're after is a smooth NAV and you invest in private equity because it's going to be smooth. And you cannot take the movement of NAVs. I have a much better investment than that for you, Emmanuel, is that instead of giving your money to a KKR, who's going to indeed smoothie NAV. But it's going to move. Okay. Instead of that, just buy yourself a few buildings in Nissan, buy yourself some farms, buy yourself some vineyards, buy yourself whatever you want. And you know what? You will be able to decide exactly how much these things are valued and how much you want to smooth them. And then you can say, oh, but it needs to be audited. You will hire an auditor and you know, I hire you Hamid as an auditor. But so, you know, I like things to be very smooth and if you're not going to smooth it high enough next year I hire Mathys as an auditor. I'm told he's smoothing things even better. So you don't need to pay all these fees to have something smooth, okay?

Just buy yourself a few things and you come up with your own valuations.

Emmanuel
I agree, but it's interesting. So what you are saying is basically because always trying to understand the motivation of the asset owners which are going in private equity, right?

Ludovic
His family offices don't care about smooth NAVs. It's their own money, okay. Like the money of a family. So I don't care that you smooth my you're working on my blood pressure or something. So the people who care about smoothing is people who have to fool someone. So the primary people is usually the pension fund. They need to fool the regulator. Right. They didn't get like funding issues. And so they are very keen on that and university endowments because they want to tell the university not to take too much money out or to leave them alone when it's not going well. Which is an interesting problem, by the way, this year, because venture capital has done so well that university endowments are on paper up like 50% or something like that. So the universities want 4% of the value of the endowment, so they want 4% of this huge increase. And then the university endowments have to explain that it's actually paper money and it's really real, so it shouldn't take 4% of that stuff. So, yeah, you do have interesting issues around NAV smoothness. So, yeah, you need to understand the motivations of people.

But again, if someone tells me I invest in price equity because I like smooth NAVs, I have better options.

Emmanuel
Okay, so super. I don't know Hamid or Mathys if you want to add final questions to Ludo before we finish the talk, we can talk for I don't know how many hours, but Ludo got also.

Ludovic
I have a tough evening to go.

Hamid
We don't have much time. Maybe the last question and a bit of advertising for EDHEC. We are one of the leading business schools focusing and going toward addressing the Climate Change and social responsibility of business and all those kind of topics. And now we are talking about private equity. So I wonder what is your thoughts are on how much of the problem is going to be solved by them or how much they are going to create problem.

Emmanuel
And perhaps a related question I wanted to ask too is you have this SFDR new regulation which have been enacted by Europe and I guess private equity funds also have to report on ESG. So it's related to what Hamit said. That means you have this momentum. There is disclosure.

Ludovic
Just before this lecture I spent an hour with an Impact investor doing a lecture on ESG and private markets. I have a talk on YouTube. You can find everything on my website. So on YouTube there is a talk on ESG and my key takeaway from ESG, it's also in my book about the importance of additionality the issue that when you intervene on secondary markets you cannot have any impact. So private equity, a lot of it is secondary markets. So like leverage buyouts and so on are typically not injections of cash in companies. So that has very little potential for impact. Although you control companies, so you could this way. But the part of a private equity which is like growth and venture capital where they inject equity into companies and control companies, there is a huge potential for impact. Now, whether they use it for the right things or not, then that will depend a bit on the funds and everything. I'm not that pleased to where they are on ESG and Impact. Again, you can listen to my talk because I have a very minority view on this one too. So it takes me a while to develop that.

But let's say that in a nutshell, 99% of what we see is greenwashing and saying that the European Union is asking them to report on ESG. It's basically asking them for an essay about why they are good people and say oh, I created jobs and I hired some minorities and I enabled people to send their children to school because I paid them a salary actually against their work. In terms of environment, I changed the air conditioning into my factories and so I'm spending less on electricity, which is a good thing. And we are trying to keep the heating not higher than 20 degrees and blah, blah, blah and it's like whatever, right? So it's just an exercise in PR and writing something about when you write a statement for your research, you just write stuff. But writing stuff doesn't solve any problem.

Emmanuel
I think this is clear. So thank you. Thank you very much, Hamid. Thank you, Mathys. And most importantly, thank you very much, Ludo Merci beaucoup. I owe you a good bottle when you come to visit to Nice. You're welcome, you know that. And before we finish, just to mention that the next session will be next Thursday and we will talk about another topic with Campbell Harvey from Duke University City. We'll talk about decentralized finance. So. Thank you. Ludo Merci beaucoup. See you soon.

Ludovic
Bye bye.

Emmanuel
Thank you, hamit. Thank you, Mathus. Merci beaucoup. See you.

Make an impact
EDHEC BUSINESS SCHOOL
[Music]

 

[Music]
EDHEC Business School
EDHEC Speaker Series - The future of Finance
SPACs: (almost) everything you need to know about SPACs

Jay Ritter
Professor of Financial,
College of Business

Thursday, 24 February 2022
6 P.M - 7 P.M Paris time

Emmanuel Jurczenko
Let's go ahead. Hello everyone, and welcome to our third EDHEC virtual Speaker Series on the Future of Finance. I am Emmanuel Jurczenko, director of Graduate Finance program here at EDHEC Business School. And it's very much my pleasure to welcome you today. Before we start, let me remind you that for those of you that who have questions, you can please send them using the Q&A box located at the bottom of your screen. We will collect as many as we can during the session and we will put them to Jay. Note that you are already able to download the Jay slides on the web page of the speaker series. And we plan, in fact, to put the recording of today's session in the comic weeks. All right, so let's move to today's program. I'd like first to introduce our two co-moderators. We have Enrique Schroth, that is professor of Corporate Finance here at EDHEC. And we also have Constantin Sengebush, that is student in our MCs in Finance. It's now my pleasure to introduce our guest speaker for today's session, professor Jay Ritter, that is an eminent scholar in the Department of Finance at Warrington, College of Business, University of Florida.

Jay is known worldwide as Mr. IPO for his work on initial public offerings. He served in the past as president also of the Financial Management Association. I'm sure that most of our audience are aware about the so called Kenneth's French website for the Fama French factors. For those of you that want or interesting to work on IPOs, my recommendation please go on the website of Jay. So we are very fortunate to have him speak today about SPACs so called Blank Check Special Acquisition Companies. So, without further ado, let me turning to Jay. Welcome Jay. And the virtual floor is yours.

Jay Ritter
IPOs traditional initial public offerings. The SPAC market has mainly been a US phenomena. Although SPAC's special purpose acquisition company IPOs have occurred in some other countries as well. In the United States, they've been around for several decades, but until about two years ago, not a whole lot of activity. In 2020, the number of SPAC IPOs quadrupled to 248, and then last year more than doubled again to 613 IPOs raising over $160 billion. And with these SPAC IPOs, they've created a lot of attention and there's a lot of controversy. And we'll explain some of the reasons for that controversy. There's good reason for them to be controversial. With a SPAC, there are certain regulations designed to protect investors, but the market has evolved due to supply and demand. And almost all SPAC IPOs in the United States involve a unit being sold where that unit is priced at $10. And it includes one share and typically a warrant to buy a fraction of a share. And importantly for the money raised in the IPO, it is put into a bank account, into a trust account where the SPAC after raising the money and it's a shell company, they have no operations, they have no business.

They're raising the money to then go out and use it to merge with some existing operating company, a private company, and take that company private by merging with this publicly traded stack. And the money raised in the IPO is put into a trust account. And if they don't complete a merger within a certain period of time, typically a year and a half or two years, they have to give the money back to investors. The warrants, which are call options that have the right to buy newly issued shares of the stock at some exercise price. If there's no merger, the warrants become worthless, but if there is a merger, that the warrants might become very valuable.

So with a SPAC, the life cycle is the same in almost all of them. You have a group of people known as a sponsor that creates this SPAC, takes it public, raises cash which is put into this trust account, and then the sponsors search for a merger, target a company to merge with and negotiate a merger. And they have a deadline to do this by they've got an incentive to complete a merger. What they do at the time of the IPO is the sponsor typically buys some warrants at a fair market price and they might invest $7 million or $10 million.

And if no merger is completed, they lose all of that. And what they do with that seven or $10 million is part of it goes to pay the costs of searching for a merger partner and negotiating a deal. And part of it typically goes to put extra money into the trust account to pay for some of the investment banking fees. And when they're negotiating with a company, frequently they also then negotiate with some private equity investors to buy more shares and put in cash in what's known as a pipe transaction private investment in public equity, where you might have a private equity firm that agrees to buy 10 million shares at $10 each and put in $100 million of cash. And then after this merger is announced, the SPAC shareholders have to vote on the merger. They can vote in favor, they can vote against. In practice they vote in favor almost all the time because after all, they own shares and they own warrants. And if there's no merger, the warrants become worthless. So they've got an incentive to vote in favor of a merger, but they have the right to ask for their money back.

Even if there is a merger, they have the right to redeem their shares. And so if they don't like the merger, they can vote to approve it and ask for their money back. And this creates a problem then for the sponsor. Typically when they negotiated a merger, part of the negotiation was how much cash was going to be delivered to the operating company that they're merging with. And if the shareholders of the SPAC redeem their shares, there's not much money, maybe no money to complete the merger. And the operating company would typically have the right to call off the merger, and the sponsors would lose the $10 million that they invested. So they have an incentive to negotiate a pipe deal to make sure there's cash, and they have an incentive to negotiate a good merger so that the SPAC investors don't redeem. Then, if a merger does get completed, the SPAC changes its name to the name of the operating company, and the stock keeps on trading with a new name and a new ticker symbol, just as if it was an initial public offering of that operating company. And the stock price might go up, the stock price might go down.

But before the merger occurs, the SPAC share price rarely falls much below $10 because the investors have the right to get $10 back. And consequently, anybody who wants to sell could sell to somebody who instead redeems for $10. And that puts a floor on the price before the merger gets announced or before the merger gets completed or not completed.

Now, when a SPAC goes public and they typically raise 100 million, 200 million, 300 million. Last year, the average SPAC IPO raised about $300 million. This year it's been a little bit lower, less than $200 million. The investment bankers that are selling the shares and the shares are mainly bought by hedge funds. They charge fees just like with other IPOs. But with a SPAC IPO, the underwriters don't take all of their fees up front. They typically charge five and a half percent, and they take 2% of that upfront. And the other three and a half percent they take if and only if a successful merger occurs. Let me just stop at this point. Are there any questions?

Enrique Schroth
Yes, Jay, I would like to ask about the underwriting fees. I mean, we know that in more traditional IPOs there is some variation in the fee here. You've mentioned the average is 5.5. So I suppose one immediate question is, is there a lot of variation around that figure?

Jay Ritter
The simple answer is no. That all of them have fees of exactly five and a half percent, whether it's raising $100 million or raising $300 million. And the reality is it doesn't cost three times as much for the underwriter if the deal is three times as big. But they get fees that are three times higher. So this is a great deal for the underwriters. It's just something that they don't negotiate about.

And I mentioned for the sponsors that they're buying warrants at the time of the IPO putting in some cash. But typically I shouldn't say typically all the time, with very few exceptions. At the time of the IPO, the sponsor sells 80% of shares to the public at $10 each and gives themselves the other 20% of the shares. They pay a little bit of money, typically half a cent per share that's worth $10. The sponsor is buying warrants and giving themselves shares. And these shares can be worth a lot of money if it's a 200 million dollar IPO. 20% of the shares would be 5 million shares, 20 million shares sold to investors, 5 million shares kept by the sponsors, 5 million shares at $10 each are worth $50 million.

So they're buying warrants for 10 million and giving themselves warrants that are worth 10 million and shares that are worth $50 million. So this is a great deal for the sponsors. You put in 10 million and you get securities that are worth $60 million. Now, these securities are going to be worthless if you don't complete a deal. So there's a big incentive to complete a deal and to complete a good deal so that the shareholders don't redeem.

Emmanuel Jurczenko
Sorry, Jay, you were mentioning that most of the shares are bought by hedge funds.

Jay Ritter
Yes.

Emmanuel Jurczenko
Is there a reason, is there a free launcher or there were a free launch. Why hedge funds are so active on this market?

Jay Ritter
Hedge funds were smarter than a lot of other people to figure out what's going on with SPACs. And we can answer this by looking at the SPAC returns for investors during two periods. One is from the IPO until the merger or liquidation, as shown in this graph. During what we can call the SPAC period, the hedge fund or other investors has bought a unit for $10. They get the shares, which they can redeem or sell or keep, and they've got the warrants. And then if there is a deal, they can vote in favor of it. And unless there is no deal and it gets liquidated, then there's a completed merger. And after the merger, it's sometimes called the deSPAC Period. It's no longer a SPAC, it's a publicly traded operating company. And let's look at the returns. But before doing that, let's just think about the three main players. We've got the operating companies that are going to merge with a SPAC as a way of going public rather than doing a traditional IPO. We've got the middleman, the sponsors, and we've got public market investors. Well, the incentives of these three main players are not perfectly aligned.

And there might be wealth transfers, there might be value created, but there also might be wealth transfers between these various players. And a good contracting arrangement would have incentives that are aligned to try, have incentives to create value. And this is one of the things that has been controversial about SPACs because the incentives might not be perfectly aligned.

Well, let's think about some of the contractual terms. These SPACs tend to have very standard contractual terms the unit with shares and warrants, a deadline for completing a merger, redemption options. And one of the things that we do in our paper is we provide an economic analysis of why do these things exist, what agency problems do they create or solve? Well, we see deadlines. Why do deadlines exist? Well, for somebody who buys the IPO and the money is put into this escrow account or trust account, their money is tied up, they've got some illiquidity there and in order to reduce that illiquidity, that's why the sponsor sets a deadline. We see the same thing with private equity deals or venture capital deals where the limited partners agree to give money to the general partner.

But there's a deadline for how long it takes to invest the money. And then there's a deadline for how long the maximum period has to be before there's an exit where investors get their money back. And the reason these deadlines are there is because the investors don't want the illiquidity of having their money tied up. But the deadline solves that problem. But it creates another problem. As the deadline approaches the middleman whether it's the general partner in a private equity deal or the sponsor in a SPAC, they've got an incentive to do a deal even if it's a bad deal, because otherwise the money gets returned or never spent and they no longer collect fees. So the deadlines solve one agency problem, but they create another agency problem. And this is what's unique about SPACs. This redemption option reduces that agency problem. As that deadline approaches, the sponsor has an incentive to do any deal as long as there's upside potential. They might invest in a deal that's expected to have a negative net present value. Otherwise they've just got to give the money back. But because the shareholders have the right to ask for their money back, if the sponsor recommends a bad merger, the shareholders will ask for their money back and the deal might wind up collapsing anyway.

So this redemption option solves the agency problem that the deadline creates and helps align the incentives of sponsors and the public shareholders to avoid doing bad deals. But why do we see the SPAC investors having a separate decision to approve the merger and to ask for their money back? It would seem that you would want to have them do a joint decision where if they approve the merger, they can't ask for their money back. But a reason we don't see the decision, the vote being just one vote, that there are two separate votes is it used to be one vote. But there was a problem. A hedge fund that owned a lot of the shares might have enough votes to determine whether the merger got approved or not. And because the sponsor is going to lose everything if the merger doesn't get approved, a hedge fund might threaten the sponsor and say we are going to vote against the merger unless you give me a side payment to approve the merger. It allowed the hedge funds to hold up the sponsor and create a wealth transfer from the sponsor to the hedge funds. The sponsors got fed up with the way the hedge funds were holding them up and demanding side payments.

And so they decided, well, we'll separate the votes. It's not a perfect situation, but it avoids this other agency problem where the hedge funds could hold up the sponsor. So this is something where regulators don't determine a lot of these features. Instead, market participants, supply and demand, have worked out these features to deal with agency problems that were or were not anticipated. And the market has been continuing to evolve month by month as we'll see later on.

Let's focus on the costs of going public, and this is where SPACs are controversial. This table shows for 153 SPACs that were completed, the merger was completed versus traditional IPOs. And there have been a few direct listings in the United States where a company like Coinbase Global has gone public without doing an IPO. They've just started to trade. And let's look at the median, the second line from the bottom there, where if we look at costs relative to proceeds, where the proceeds would be the amount of cash delivered to the operating company for the median SPAC, the costs of doing a SPAC merger are 48% of the money they receive. And these costs are underwriting fees because if it was a 200 million dollar SPAC IPO, the underwriters at five and a half percent are going to get $11 million in fees. But what if 90% of the money gets redeemed? Well, then there's only $20 million in cash left after redemptions, but the underwriters are still getting $11 million in fees. So these fees as a percentage of the cash delivered are huge.

What about with traditional IPOs? Well, the costs are still high, but they're nowhere near as high as with a SPAC. And with a direct listing, they're really cheap. So this raises the question of if SPACs are really expensive, why do people use them? Why do some companies choose to merge with a SPAC rather than doing a traditional IPO? There's got to be some advantage to offset the higher costs on average.

Well, this is where the economic roles of sponsors come in. The sponsors are middlemen, but they also can create some value. Just as like with private equity or venture capital. The middlemen there the general partners, they're not only providing cash to the operating company, they're also providing advice. And some sponsors are better than others. When sponsors are looking for a merger partner, the sponsors frequently target a specific industry. You might have a sponsor that has an auto industry executive as part of the team, and they might be trying to merge with an electric vehicle company or an autonomous vehicle company, some company in the auto industry where the sponsor knows something about the industry and potentially can create value by offering good advice in terms of what the business strategy should be.

What we see is the vast majority of SPACs. When they announce we're going public, they announce we are targeting a certain industry or a certain geographic market. Like we are focusing on merging with a tech company in Europe. Or a consumer business in Latin America, rather than we're just going to merge with some company and we don't know what industry it's going to be in. You rarely see that.

Emmanuel Jurczenko
And Jay, with respect to that, this is one question which have been sent by one student. Are there some industries that are of particular interest for SPACs that mean we are looking the merger? Is it more with respect to iTech or what are the coverage of the industries that you find with respect to SPACs?

Jay Ritter
This has varied over time. Until the last year or two. It was actually the case that there was a wide variety of industries. Although in the last year or so, more of them have been in the tech sector and a pretty high proportion of the operating companies have been venture capital backed. There's one industry, however, that has been much more likely to do a traditional IPO and not a SPAC merger and that's biopharmaceutical startups, drug company startups that are doing research and development. They rarely merge with a SPAC. These are typically companies that are very R&D intensive. They have a high cash burn rate, they probably won't have revenue from product sales for many, many years. And rather than doing a SPAC merger, they typically do a traditional IPO and will probably do some follow on equity offerings as they burn through that cash.

But we continue to see evolution. There have also been a fair number of autonomous vehicle startups or electric vehicle startups that have gone public with a SPAC merger. Now, what are some of the other advantages of merging with a SPAC? Well, one advantage that is frequently talked about is it might be quicker to merge with a SPAC than do a traditional IPO.

In our paper we provide information on how long does it take between the merger announcement and completing a deal. And we show that it actually isn't any faster on average than a traditional IPO. Now, it might be for some companies in the United States. If a company goes public, it has to have audited financial statements. If a company merges with a publicly traded company, including a SPAC, it doesn't have to have audited financial statements. So if a company did not have audited financial statements, it might be quicker to merge with a SPAC and then get the audited financial statements afterwards. But for other companies that were already preparing to do an IPO and already have audited financial statements, we show in our paper that there's no evidence that it's actually faster. There's also been a lot of discussion that in the United States, the litigation environment, the chance of a lawsuit is different if there's a merger than if it's a traditional IPO. And this argument, which is sometimes called regulatory arbitrage, has been used to say that there's less lawsuit risk if there's a SPAC merger than with a traditional IPO, and that might be one of the reasons for going public.

Enrique Schroth
I have a question. Would it follow from this reasoning that SPAC targets are firms that self select themselves as not wanting to be audited?

Jay Ritter
Well, this is one of the controversies. Some people have argued that the higher quality companies aren't merging with SPACs, that it's low quality companies that couldn't get a big name investment bank to do the IPO, that instead they wind up doing a SPAC merger. And investors haven't figured out that this is a low quality company. And a reason that this story has some plausibility is that after the merger, a lot of these SPACs have had very low returns for public market investors. And we'll look at that shortly. One other argument that's been made for why some companies would want to do a SPAC merger is the so called regulatory arbitrage that when there's a merger, companies normally announce forecasts of revenue and profits. When they do an IPO, they don't put it in writing because if things don't work out, there might be a higher chance of a lawsuit in practice. I'm not sure how convinced I am about this argument because while it is true that for an IPO, they don't put the forecasts in writing, what happens in practice is the company tells the underwriter's analyst what our forecasts are going to be. And then when somebody asks the company in the roadshow, what are your forecasts? The company says, well we don't give forecasts, but the analyst at our underwriter is making the following forecasts. And all of the institutional investors understand that this is the way the game is played, that the analysts got the forecast from the company. So the reality is that whether it's an IPO or a SPAC merger, the institutional investors are getting those forward looking statements from the company.

Now, before we get to the returns in a moment, one of the things that we do in our paper is we also focus on co-variances. And this might explain why some companies choose to use a SPAC even though on average it's more expensive. And that's that if the merger looks good, everything goes according to schedule. If the merger looks weak and investors have a high rate of redemption, in many cases the sponsor and sometimes the underwriters give up some of their portion of the pie and so they're risk sharing with the operating company, with the public market investors. And that risk sharing approach, the covariance that if things look good, everybody's going to be happy. If things look bad, the sponsor gives part of their part of the pie to the other parties that might make it more attractive because of the improved risk sharing with a SPAC. We provide some evidence that there is some of that going on.

But let's quickly look at the returns and this gets back to the question of why hedge funds buy these IPOs. Historically, if you bought in the SPAC IPO and then at the time of the merger or liquidation, you either redeemed or you sold your warrants or shares. If the price was higher, the average annualized return that the IPO investor got was 15.9% per year. And because of this redemption option, you've got a money back guarantee. So there's no chance of losing money. And earning 15.9% with no risk of loss is a great deal. This is why the hedge funds were happy to buy these SPAC IPOs. Other people just hadn't figured this out what a great deal it was.

Now, what about at the time of the merger? Well, typically the hedge funds sell their stock. If they don't redeem, who do they sell it to? Well, sometimes to retail investors, to individuals who then will own it in the deSPAC period.

What about warrant returns for the warrants? Let's look towards the bottom there. The average one year return for the warrants after the merger has been 68%. That's a huge number. These warrants have been really good investments. That's an equally weighted number. On a price weighted number, the average is only about 25%. And that's because some of the highest returns have been on low price warrants. Warrants that were deep out of the money, and most of the time you wind up losing money. But sometimes the warrant investors have made hundreds of percent, bringing the average up. What about for the common shareholders? Well, on average, equally waiting in the year after the merger, they've earned a return of minus 8.1%. That's horrible. And this was during a bull market period over the last decade, where on average, the stock market went up 16% per year. So if you owned a SPAC that went down 8% and you could have earned 16% in the market, you underperformed by almost 25%. This has been horrible. One of the reasons are controversial.

Enrique Schroth
Yeah, I have a question on this. Is there any evidence of when do the hedge fund investors or the sponsors themselves exit during the deSPAC period? Do they wait until these negative performance of the merger after one or three years is achieved, or do they leave early?

Jay Ritter
Usually at the time of the merger, if the hedge fund did not redeem, they sell their stock in the open market. But if there are high redemptions, high redemptions are a predictor that the returns will be low. People are redeeming because they're saying, we don't think this merger is very good. If you wait instead of equally weighting the deals by how much money is left in. So if it was a 200 million dollar IPO, but 90% gets redeemed, only $20 million is left by investors. If the redemptions are 0%, $200 million is owned by investors. And if you weight by that cash, the average return is plus four and a half percent, much higher than the minus 8% equally weighted. It still underperforms the market on average, but nowhere near as bad. So this is saying that investors on average aren't stupid. If they think this is going to be a bad investment, they get out. If they think it's going to be a good investment, they stay in. And on average they're right.

Emmanuel Jurczenko
And just Jay, a related question, so just to be clear, I understand well. With respect to the warrants, in this case, they can keep it, right. So I can decide as a hedge fund to redeem, but I have the possibility to keep my warrant. And potentially if there is an upside.

Jay Ritter
And a lot of the hedge funds do keep the warrants. And they also might decide, they look at each deal. A typical hedge fund buys a lot of stock in a given SPAC that they might put in $20 million. And so they have an incentive to pay attention and evaluate each deal and vote in favor, vote against redeem, not redeem. The question also came up a minute ago about what about the sponsors, do they get out quickly? And the answer is no, that at the time of the merger, normally the sponsors agree to a lockup and the operating company shareholders agree to a lockup. So once the stock starts trading, they're not able to sell right away. Typically they've got at least a six month lockup. Sometimes the sponsors even agree to a five year lockup, which helps align incentives. On the other hand, the public market investors, they can get out right away if they want. But this actually creates a little bit of a problem. You might have a 200 million dollar merger which gave a pre money valuation to the operating company of $800 million. And so you've got a market value of the company of a billion dollars.

But if there's most of that stock is owned by the operating company shareholders or the sponsor, if there have been high redemptions. And the public float might be only $20 million on a billion dollar company. So with such a small public float, the stock price might be pretty volatile right after the deSPAC occurs.

Now, the SPAC market has been evolving. It's been too favorable for the SPAC period. Investors, they got this 15.9% return on average. The sponsors, even if they have to give up some of their shares or they have to agree to long lockups on average, the sponsors have been doing really well. This is why so many IPOs have been getting created. Who loses? Well, partly the public market shareholders lose if they didn't redeem and the stock price falls. But also for the merging company shareholders, they might have agreed to merge at $10 a share. But if they're locked up and they can't sell until the stock price has declined to $5 a share, their $800 million of value that they negotiated at the time of the merger is worth only $400 million by the time they're allowed to sell. So what looked like an attractive merger to them might not turn out to be as attractive.

And this is possibly one of the reasons why a lot of SPACs are finding it difficult to find companies to merge with. The operating companies are saying, look, the track record hasn't been that good, the costs are high, some companies might want to do it, but other companies are saying, no, we want to do a traditional IPO.

So let's just look at how the SPAC market has been evolving over the last three or four years. The blue dots here give the dilution per unit. And this is our measure of the SPAC IPO. It's a unit of a share plus a warrant. How much of a warrant? Well, it might be a warrant to buy one share, or it might be a warrant to buy half a share. If it's a warrant to buy one share, we call that 100% dilution. If it's a warrant to buy half a share, we call that 50% dilution. Four years ago, the average amount of dilution was about 80%. That the typical SPAC IPO was giving the investor a warrant to buy about 0.8 shares. But as SPACs became more popular, it became easier to find IPO investors. More hedge funds wanted to get in, more other investors wanted to get in.

And the sponsors started to say, well, look, we can find investors and we don't have to offer them as generous a term. If we can find investors who are willing to buy a unit that gives them a quarter of a warrant rather than three quarters of a warrant, why should we give them three quarters of a warrant? And by a year ago, a lot of the time they were giving only one quarter of a warrant. But then, as the market softened towards the end of last year, the sponsors have had to be more generous again. And indeed, in the last month, that warrant fraction has gone up to more like 60 or 70%. It's gone back up again.

What about how much cash is put into the trust account? This is over the last two years, and this graph comes from Gritstone Asset Management, which is one of our data providers. Our other main data provider is IPO rather spacresearch.com. And a year ago, almost all of the units involved, the sponsor saying, we are selling it at $10. After the 2% underwriter fee, there's going to be $9.80 left. 2% is taken out, but we'll put in that 2% extra. So you get $10 rather than $9.80.

What happened at the end of last year is in order to attract investors, the sponsors started to say, well, we'll not only give you $10, we'll give you $10.10 or $10.20. They've been overfunding the trust account. And so if you redeem, you'll get more than $10 plus interest. And indeed, in the last month, almost all of them have been giving $10.20 well, that also makes it less attractive for the sponsor, because instead of putting in $7 million up front, they've now got to put in $11 million up front to overfund the trust account. And they risk losing all of that if a merger is not completed. The pipe investors have also been getting burned in the last year, and they've become more aggressive when a merger occurs. They've been saying, well, yeah, we'll buy shares at $10, but only if you the sponsor give us some of your shares, make a wealth transfer from you to us. Otherwise we won't put in the cash, and there's a chance that the deal will collapse. And so the sponsors are getting squeezed. It's still lucrative for the sponsor, but not as lucrative as it used to be. The market continues to evolve.

So let me conclude, and then we can take some more questions. A SPAC merger is a more expensive way of going public than a traditional IPO. Our paper shows that other papers have showed that as well. A SPAC merger has costs and benefits. It's more expensive, but it might have some advantages regulatory arbitrage, better covariance properties, sponsor economics, or rather the sponsor contribution. Various other things may be faster for some companies, but our paper shows that some of these advantages might not be as big as people sometimes think they are. And the regulatory environment might be changing as well. In the past, the SPAC IPO investors have gotten really good risk adjusted returns. But this number is going to be smaller in the future than it used to be. Why? There are too many SPACs out there. More and more of them are going to wind up liquidating. The warrants will become worthless, and the IPO investor invested $10. And a year or two later they're just going to get $10.20 back. They're only going to earn 1% or 2% per year rather than 15.9% per year. So what was too good is no longer so attractive.

The deSPAC returns have been really bad for the shares, and this actually has been even worse in the last year. So this is not an equilibrium. Investors aren't continuing to be willing to buy these shares and hold them if they get really bad returns. And recently the redemption rates have been really high. On the other hand, the warrants have been very good investments, and the SPAC market continues to evolve. Supply and demand continues to evolve. But I think there's been a bubble. A lot of people think there's been a bubble. SPACs might continue to coexist with traditional IPOs, but I don't think there's going to be a huge boom in Europe, and the boom in the USA. I think will be, you know it's already contracting, but not as rapidly as I would have expected. So let me stop there and open the floor to some questions that have come in.

Emmanuel Jurczenko
Okay? Thank you so much, Jay. Super presentation. So, as I said, so now I will let Enrique to jump in and it will be followed by Constantin that will ask you several questions. Enrique?

Enrique Schroth
Thank you, Emmanuel. And thank you very much, Jay, for the presentation. I'll try to be very brief, at the risk of oversimplifying, because this is actually a very nuanced paper, but let me just try to cut the chase and then open to Constantin and other questions. I read this paper, as one should, which is, I really want to know what the SPAC boom is all about, because it's everywhere. And to me it helped a lot. Every time I was thinking, AHA, this is where the value or the returns are coming from. The paper just shut that door to me and said, no, it's not this. It must be something else. And we saw it during the presentation, actually, at the very end. I was quite, in a way, confronted by your interpretation. This seems to be an off the equilibrium transition, and we may soon arrive at the situation where the returns to the sponsors are just not sorry to the investors are not as spectacularly high as we have seen. So it's probably a case where they've been given too many guarantees. I go home much comforted by that take on what has been going on than any other taking which they have discovered a solution to firms who can't IPO and sponsors are adding a lot of value by bringing these firms public with their advice. It seemed to me that it had to be so much value that I didn't see occurring in the deSPAC period in the returns on the merger. But I suppose your interpretation that we're still just not in the equilibrium is something I take away. But do correct me if I'm wrong, Jay, if I should interpret it a different way.

Jay Ritter
Yeah, I think you're right. One thing that's been bothersome is that other authors have documented that deSPAC returns were bad 20 years ago, they were bad 15 years ago, they were bad ten years ago, they were bad five years ago. They've been even worse in the last year. And it's taking longer than it should to reach an equilibrium where the IPO investors, the hedge funds, earn a competitive rate of return, where the sponsors aren't earning huge returns, where pipe investors are earning a normal risk adjusted return. And most importantly, public market investors in the deSPAC are earning a competitive return rather than a negative risk adjusted return. But while there have been regulatory changes, the main thing that's been going on is public market participants, pipe investors, underwriters, et cetera, are demanding changes and getting changes.

Enrique Schroth
Constantin, please.

Constantin Sengebush
Thank you very much for your presentation. That was really interesting. I have one question, I have a few questions, but let's start with the first one. You talked about edge funds, the interest of edge funds in this kind of structure, so in SPAC. And I have a question regarding this. So we have seen that SPAC sponsors and secondary private equity have some similarities in their business operation. So do you see this kind of new structure? So the SPAC sponsors as kind of competitors to secondary PE and if yes, what could be the consequences? For instance, reduction of the fees?

Jay Ritter
Yeah, SPAC sponsors are both competitors of private equity firms. But some private equity firms use a SPAC merger as an exit for one of their portfolio companies. So in one respect sometimes their competitors and in other situations they're complementary to each other and they continue to coexist. But one feature that exists with these SPAC IPOs is the SPAC sponsor keeps 20% of the shares and this number just has not been changing. What has been changing is the fine print at the merger agreement where pipe investors are demanding wealth transfers or they're demanding longer lockup periods for the sponsor. Or they're saying your sponsor shares will only vest, you get to keep them if and only if the stock price goes up. And this helps align incentives, it reduces the sponsor's economics, it's been too big. But just like with venture capital and Private Equity where it's still common to have a 20% carry, that's been very sticky. But when you look at the fine print, the middleman gets squeezed. And that's been happening in the SPAC market that if you just look at the first page, things haven't changed. But when you look in at the fine print, there have been a lot of changes going on.

Constantin Sengebush
Thanks a lot. And regarding this, it's true that there were a lot of change. I read a lot, I did a bit my research, I've seen that recently the SEC implement new regulation to put more border to this kind of structures. I have question about this. So we know that the investors can go out before the merger. So what about the regulatory framework regarding US accuracy and transparency? So how do SPAC investor is there a lot of rules, how does it work exactly?

Jay Ritter
Well, the US SEC has been under pressure to do something and they have been making some changes, but mainly focusing on disclosure. But whether or not they've been doing anything, as I've mentioned, the market has been changing. For instance, how much the initial trust fund is funded. Whether it's $10 or $10 or $10.20, that's not regulators forcing changes there. Instead it's the IPO investors. The hedge funds have been saying we're not willing to buy this stuff unless you offer us more. Because while we've been getting great returns in the past, we think there's an oversupply and the returns aren't going to be as good and therefore you need to give us more, which comes out of the sponsor pockets. And that's one of the ways that the market has been changing, reducing the sponsor economics, making it less attractive for them. And it's also the case that for sponsors, investors are paying more attention to who the sponsor is. Is it a sponsor with a good track record? Is it a sponsor who's got a lot of industry expertise? Or is it some nobody that we've never heard of that doesn't have a good track record? Those sponsors are having a lot of trouble getting a deal done. And the sponsors with good track records are still able to get a deal done, but they are having to give more to the other participants.

Constantin Sengebush
Definitely. So it's mainly about analyzing the management team. I definitely understand this. And it's really small job of a big company such as hedge funds. Nevertheless, I think that a lot of investors, such as retail investors, would be interested to invest in this kind of product. So my question, my personal question is this kind of structure open for investment to retail investor? And if yes, can it be a kind of innovative way for investor to invest in early stage companies?

Jay Ritter
Well, in our paper we show that actually on average, the age of companies and the distribution of companies going public with the SPAC merger has been no different than with traditional IPOs. So some of them have been quite old, others have been younger. The younger companies haven't done that well. And that tends to be true with the IPO market as well. So one of the things that proponents of SPAC sometimes talk about is, well, this allows individual investors to be able to get into companies at an early stage rather than just the wealthy investors in venture capital funds. But the evidence is not very strong that this is good for investors. In the last year there have been more younger companies doing SPAC mergers. But this raises the question about the quality. In fact, I was just looking yesterday at some specific deals where both IPOs and SPAC mergers, where I know some of the people involved, where the company was very young. And I said to myself, why didn't this company, this operating company, do another round of venture capital financing? Why did they rush to do this IPO or this merger with a SPAC?

Jay Ritter
And I'm pretty sure the answer is because quality Venture Capital firms looked at the company and said, we're not willing to invest in you. We don't think you're a very good company. When these companies did go public either with a SPAC merger or with a traditional IPO, they did not have high quality underwriters or high quality sponsors. So investors are paying attention to the quality of the people involved. But not all retail investors are sophisticated. Unfortunately, there are some that are not financial experts, they're not industry experts. And that's where there might be a role for regulators to insist that there might be more warnings to prevent some retail investors from making mistakes.

Constantin Sengebush
That's very clear. Thank you very much for your answers.

Emmanuel Jurczenko
Thank you. Constantin, perhaps I will take one question from the Q&A box. Are SPACs sponsors by any chance affiliated to hedge funds or PVC funds?

Jay Ritter
Usually the sponsor is not a hedge fund, but there's actually quite a variety of people. The returns for the sponsors have been so good that it attracted a lot of entry. You've got some sponsors that have done many SPAC IPOs. These typically are people with good track records where some of the early deals worked out well. But some Venture Capital funds have become sponsors, some entrepreneurs have become sponsors. And sometimes you get a team. One person might be a financial expert, another might be an expert on the auto industry who's a retired auto industry executive. You sometimes get a team of people coming together. But a lot of operating companies that have a market value of maybe 500 million, a billion, they find that lots of SPACs are approaching them and they might have 20 SPACs approach them and ask, do you want to consider a merger with us? And these operating companies, a lot of the times they're saying, hold it, we've got 20 SPACs to choose from. They've got money. But let's also pay attention to the quality of the SPAC sponsors. Is this somebody who's got a good track record? Is this somebody with industry expertise that can add value to our company?

And they might tell 15 of them, we don't want to even talk with you, that there are five other sponsors that are much better quality, that are much more likely to add value to increase the chance that this will be a successful merger, where we will be successful. And after all, it's our shares that are locked up. We've got incentives to make sure that this works out well, where our company creates value for shareholders and creates value for us because we own shares in the company.

Emmanuel Jurczenko
Thank you, Jay. Perhaps the last question compared to PE, where majority control in their portfolio companies is important for the monitoring of the management strategy. The question is, what is the role of the sponsors after the measure? Do they have the capacity, ability to influence the management decisions? So basically the comparison between the sponsors and a PE back portfolio company yeah, it's.

Jay Ritter
Very frequently the case that the sponsor is going to be a minority investor. We're very frequently one of the sponsor team becomes chairman of the board of directors. So they're involved in offering strategic advice, but they don't become a full time employee. Instead, the existing managers of the company continue to be the managers that deal with operations. But they've now got a sponsor who's on the board of directors who can offer strategic advice as well as having their interests align with all of the shareholders, where they all want the stock to do well. Because, as I mentioned before, very frequently the operating company shareholders and management get locked up. The sponsor gets locked up. Sometimes employees of the company that own a small number of shares or stock options are not locked up. So it might be the executives with inside information are locked up, but the lower level employees are allowed to get immediate liquidity.

Emmanuel Jurczenko
Okay, so thank you very much. Thank you, Jay, for sharing your expertise. Was a great session today. On behalf of EDHEC, I want also to thank to all the participants, there are other people I want to thanks obviously, Enrique, Constantin and our team, Emilie from the finance graduate programs, Christophe from IT department we got also from communication. Before I close this session, just a reminder. So today presentation will be available. The recording will be available on our dedicated web page. And just to mention that there will be a next session that will be on the Thursday 24th March. It will be Ludovic Phalippou that have a pretty provocative title for his presentation. Will Private Equity Kill them all? So thank you. Thank you so much, Jay. It was a pleasure and see you soon. Thank you very much. Thank you, Jay.

Jay Ritter
You're very welcome. My pleasure. Thank you.

Make an impact
EDHEC BUSINESS SCHOOL
[Music]

 

[Music]
EDHEC Business School
EDHEC Speaker Series - The future of Finance
SUSTAUNABLE ALPHA: Doing well by doing good?

Andrew Ang
Managing Director & Head of Factor-based Stratégies,
Blackrock

Thursday, 27 January 2022
6 P.M - 7 P.M Paris time

Emmanuel Jurczenko
Okay, so let's go ahead. So hello everyone and welcome to our first edition of the EDHEC Virtual Speaker series session on the Future of Finance. I am Emmanuel Jurczenko, I'm the director of the graduate Finance program here at EDHEC. And I'm very pleased in fact, to welcome you today for this first session. So, as we mentioned, the objective of this new speaker series is to address the most recent evolutions in the financial industry.

We aim to do that by in fact bringing to you top-notch industry experts and also top-notch academic scholars. And the objective here is to advance a study in the practice of finance. And this is related in fact, to the motto of the EDHEC Business School, which is Research for business and to make an impact. Okay, so before we start this session, let me share from some housekeeping items. All participants must have their microphones muted and the videos must be turned off during the presentation for those of you who have questions. So we will take in fact, your question. You have to put them in the Q&A box in the bottom of your screen and we will collect as many as we can and we will give them to Andrew. To Andrew, sorry, after his presentation and the discussion with the co-moderators.

Note that you will be able, in fact, to download the slides of Andrew. We'll post them after this session and we hope also to be able to post the recording of this zoom session. All right, so let's move to today's program. So first I would like to introduce our co-moderators. So we have the pleasure to have Abraham Lioui, which is a professor at EDHEC. I was saying to Abraham that Abraham is well known because he is changing the ESG factor in the markets. And so we are very happy to have Abraham. Abraham is one of our experts in ESG investing at EDHEC. We also have Lauren Deville. So Lauren Deville, which is also a finance professor at EDHEC and which is the academic director of the Finance MSc program here. And last but not least, we are very happy to have Etienne COLTA. So Etienne is one of our students to our new MSc in Climate Change and Sustainable Finance. And he will ask Andrew some questions related to his presentation. Now I'm turning to Andrew. So obviously it's a very great pleasure and I'm very proud in fact, to start to open this series of talk with Andrew.

The first I contacted in fact, for this new series was Andrew. And Andrew right away was positive. And I'm very, very happy, I'm very honored to start. And again, a big thank you, Andrew, for accepting this invitation and for opening this new series.

So Andrew is the head of BlackRock systematic. Well solution. He's the head of the factor based strategic groups. He also serves as senior advisor to BlackRock retirement solution. But I'm sure that most of the people are aware that Andrew is not only a very renowned international industry expert, Andrew is also a very well-known financial economist. Andrew got many lives and sometimes ago he was a professor of Finance at Columbia. So he have published more than 100 top academic articles. He have written some similar papers, especially with respect to the low volatility factor, but also have written on macro and the link of the macroeconomy with respect to the financial markets, on real estate, on private equity, also. So, different topic. There is also his book, his 2014 book that most of the finance professor, practitioner students have used, which is the asset Management systematic approach, factor investing. It's a reference for scholars, students, practitioner. Andrew I've really popularized the so called factor investing and I remember when I was teaching to my students using the so called Analogy between nutrition facts and the factor exposure. So, very important contribution to the finance world.

So we are very fortunate to have today Andrew.pecuniary and Andrew will talk about sustainable investing. And obviously there is a very important topic. Is it possible to do well by doing good? Is it possible in fact, to create alpha? Is it possible to achieve at the same level pecuniary and non pecuniary goals? So, without further ado, let me turn into Andrew. Welcome, Andrew. Again, thank you for your time, thank you for your presentation and the virtual floor is yours. Thank you, Andrew.

Andrew Ang
Well, thank you very much and it's a real pleasure to be with you today. I only hope that we can do this in person very soon all together. So I would like to talk about three things today. I want to talk about sustainability. Sustainability as a form of alpha to generate returns and we'll go through some of those examples. I'd like to talk about the relationship between sustainability and style factors, which some of the people that Emmanuel introduced today have had some very, very good work on. And then I'd like to talk about positioning portfolios for the net zero transition or the movement to a world of lower carbon to achieve net zero emissions, hopefully by the year 2050.

So, as Emmanuel said, I used to be a professor at Columbia University and for the last couple of years I have been at BlackRock. I head a group called Factors Sustainable and Solutions. And as part of BlackRock Systematic, we manage approximately $220,000,000,000 of funds within our group all along quantitative lines. So let's talk about doing well by doing good. So hopefully you can see the screen. So let's turn to these three things, right?

So factors and sustainability. Sustainability is a source of returns in their own right and then the climate transition. Okay, so I think one way to frame thinking about investing in sustainability are these three terms you can align with someone's values. Those are typically implemented by screens. You can go further and build sustainable insights into your portfolio uplift ESG, for example, or obtain certain carbon outcomes. The most exciting one to me is the last one, where we can enhance our portfolios by using sustainable data as a form of alpha. But let's talk about the second one first, which is to uplift portfolios with sustainable insights. Now, I've written a fair number of papers on factor investing buying cheap stocks, finding high quality names, finding trends or momentum. And then we can also have gravitating to safety with low volatility strategies. If you're a factor investor, it turns out that you are already an ESG investor with two factors having pronounced exposures to sustainable scores low volatility and quality. In fact, a lot of EDHEC researchers have documented this relationship between factors and ESG.

Now, we can actually build these insights into a portfolio by actually looking at factors together with ESG. I call these best friends because I'm an academic. I would think about Kahneman & Tversky, but if you really like it's about Batman and Robin or Simon and Garfunkel. Now, the top right hand quadrant is really where we want to be. We can obtain higher ESG scores than the market portfolio, and we can also reduce our carbon emissions relative to the market portfolio as well. A balanced multi factor portfolio also sits in that quadrant, so we can have our cake and eat it, too. We can look at factor premiums combined with sustainable outcomes. And there are now a few products in the market that build in these two things together ESG minimum volatility, ESG and quality, ESG and momentum, ESG and value. It's interesting that the performance of these is pretty much unchanged, in some cases, actually enhanced by building in these sustainable insights. All right, now let's go to what I promised would be the most exciting part of looking at ESG, and that's as a form of returns, we can integrate ESG data into factor definitions and also use ESG as a form of returns or alpha in its own right. Okay, so let's have a look at some of these signals.

And this is research that we've published over the past couple of years. Now, patents are a great source of information. They represent intangible capital. I actually have a patent, but in finance, they tend not to be so valuable. But in other industries, especially in biology or computer science, patents are extraordinarily valuable. Now, patents are often a culmination of research and development. Spending that time zero that you can see is actually when a patent is awarded, and you can see R&D Spending ramp up before the patent is awarded. That makes sense because you actually have a patent coming out as a result of investments in research and development. And then there's also spending after that patent is awarded, where you do need to monetize the technology that you've developed. So patents are form of investment, and as investments, they are risky. They represent fundamental value. But it's fundamental value that usually is not captured on a balance sheet or directly captured by an earnings income statement. Now, patents are a source of alternative data. They consist of text and diagrams, and there are hundreds of years of patents dating back to the 17 hundreds.

What we need to do is to link the patents back to companies and tickers that you can trade. That's actually quite difficult. We actually have to form some links of how we can get that patent information of inventors and assignees of subsidiaries, often which file the patents back to parent companies, which you can ultimately trade. We perform natural language processing techniques to try and match entity names and to match assignees and inventors with a parent company whose ticker you can ultimately trade. There are green patents, brown patents, and white patents. The green patents are those patents that are filed under fields corresponding to UN sustainable development goals. They're listed here. There are other types of patents as well, but we're particularly interested in these partly because the patents are themselves a form of intangible capital, but also because these represent not only important goals for society, but highly profitable investment opportunities. If we can deliver clean energy, that is not only great for the planet, it's also a very, very good investment opportunity. Now, since patents represent fundamentals, we can then use patent information as part of a value signal, and that's exactly what we do, and we call that green intangible value.

Here's another example, using ESG data that we can build into factors. This one is on corporate culture. And if we've learned anything over the past two years during COVID it's that a company is not only its locations of where people come to work, the most successful companies have had really good company culture, but culture often is a qualitative metric. It's hard to quantify, and that's actually the real major advance. We can build machine learning approaches, especially using natural language processing, to estimate or quantify corporate culture. We treat it as a form of non financial quality, and we start off, as we often do, with some academic work. This is a paper written by three professors. Luigi Zengales is a well known finance professor at the University of Chicago. Start off with five pillars innovation, integrity, quality, respect and teamwork. And then what this process will do, called word embedding, is over a training sample. It will build a dictionary of related words. For example, integrity could be related to ethics and accountability, honesty, fairness. Then we go to conference call transcripts and we count the incidences or frequency of these words mentioned in the conference call transcripts, adjusted for things like how often these conference calls occur, the length of the conference call, and other things.

This is now a quantitative way to measure corporate culture, and it's related to other more traditional quality factors that use only balance sheet or earnings income statements. Corporate culture was one of the best signals that we had in 2021. Really, it's actually in the top two signals that we run. And BlackRock, systematic companies with the best corporate culture tend to have done very well during our periods of social isolation and social distancing. So what we've shown so far is we can use factors and ESG together. And we've also shown that you can use ESG to enhance factor definitions and start to treat them as a form of alpha. Now there is another source of ESG which is very important, and that's sustainability. And that's where I want to turn now. There are also alpha opportunities in sustainability and it's very important for us as a society to reduce the amount of carbon emissions in the atmosphere to limit the average temperature rise.

One very important piece or the framework is the Paris Agreement, which was signed in 2015. The Paris Agreement builds in a series of scenarios or pathways where we want to minimize the temperature increase that requires taking carbon emissions out of the atmosphere.

The EU has specified a series of portfolio requirements which help investors to achieve this. This is the EU TEG, or the Technical Expert Group in Sustainable Finance. There are also guidelines that have been issued by the Institutional Investors Group on Climate Change, the IIGCC, and we can use these guidelines to help adapt our portfolios. We can go further and help drive the transition to a low carbon future. There is here also an opportunity to create alpha. One example is to look at carbon intensity emissions. These are defined as scope 1 or 2, sometimes scope 1, 2 and 3 emissions divided by sales or enterprise value. Scope 1 are carbon emissions that a company directly is responsible for. Scope 2 includes now the carbon emissions associated with the energy used in production and scope 3 are all the carbon emissions along a company's supply chain. The graph really speaks for itself. We can look at companies with the lowest amount of carbon intensities and they outperform, as shown in the blue trending line up. Companies with higher emissions have actually trended low. They've lost money. Now, this is really important because we can now use a climate signal to generate returns in our portfolio.

Now there is some academic work which these papers here by Patrick Bolton and Marcin Kacperczyk, they document that the relationship seems to go the other way. In our experience, it's been the low carbon emission firms that have outperformed. We've done some reconciliations and our relationships are predictive. The Bolton and Kacperczyk papers, they tend to be contemporaneous. Predictive really is what we've seen out of sample in our live portfolios.

The economic story of what's going on here is that it's not so much carbon emissions driving higher returns, they're correlated with high future returns. And I think the mechanism is that firms that are really focused on being efficient at all stages in their production line, producing goods or producing services, they are naturally going to want because they're most efficient, focused, want to gravitate to more efficient carbon reducing technologies. And so what's really driving all of this is the notion of quality. And we treat this as one implementation of a quality factor. You can do good and you can also have financial outperformance.

Now, let's turn to the last topic, which is on achieving the net zero outcome in our portfolios. And this is the most recent work that we've just been accepted to two journals. There's also related work that Emmanuel is organizing in a book to appear called Climate Investing.

Net zero is all about approaching by 2050 the same level of carbon emissions as before the Industrial Revolution, in particular during the years 1850 to 1900. That's the so called net zero outcome. And the big number is actually Bill Gates. He has written a book on climate change, or how to Avoid a Climate Disaster is actually the name of the book. And Bill Gates's favorite number is 51 billion. 51 billion is the amount of tons of carbon dioxide equivalents that we put out every year. We need to go from 51 billion to zero and then we will have the same level of carbon emissions as 1850 to 1900. Now, if we do this, we will limit average warming to 1.5 degrees, as scientists have documented in many thousands, tens of thousands of reports that have been collated and summarized by the IPCC. The way to do it is that we need an initial fall of carbon emissions by 50%. That's right, 50%. We need to cut carbon emissions by 50% and then we have further decarbonilizations by 7% year on year.

And you can see we approach basically zero by the year 2050. The base year for those decarbonizations is 2020. So this year we will have obtained 50% reduction plus two times 7%, appropriately compounded in the amount of carbon emission reductions achieved in our portfolios. Now, there are some other requirements to achieving a net zero or Paris aligned portfolio as defined by the EU TEG and the IIGCC, and some of those are listed here. Not only do we have to reduce carbon, but we also want to do this without divesting from any sector. Now, it's way too easy to reduce your carbon emissions by just throwing out high impact sectors like energy or utilities. That actually doesn't solve our problem, they're essential for society. We need to move the whole world, the whole economy, towards a lower carbon future. And we can do this within each sector. We need to be forward looking and then we need to favor companies with higher green to brown shares.

That's exactly what we've done in our portfolios. 50% reduction, 7% year on year. We don't divest from any sector. And we have some other requirements as well. There's some technical requirements the sectors have to be defined by the EU as in NACE. And we also want small amounts of tracking errors relative to mainstream market cap benchmarks.

We've then added our sort of alpha signals associated with climate. I already talked about carbon intensity as a form of quality, and I've also talked about patent information or green patents. These are ESG data and information that we can use to generate additional alpha in our portfolios. Here's some of the research that we've done. We can obtain 50% and further 7% decarbonizations hugging a traditional market cap benchmark, and we can also achieve some average outperformance that's been historically experienced in the data by the climate alpha signals. We can do this in every region, we can do it in DM and EM in Europe, and we can do it in global portfolios.

One very important aspect of these Paris aligned or net zero portfolios is I said they have to be forward looking. Companies have now increasingly broadcast their ambitions of reducing carbon in their activities, and these are captured in science based targets reports, SBTIs. Other reports include TCFDs. So an increasing number of companies, several thousand over 2000 companies, have now voluntarily set climate targets. We can actually use this information. We've collected all of these data, we actually have scraped it, we actually look at the text and we certainly look at the targets. Companies that have set these forward looking emission targets have also outperformed, and they've done this in every sector.

Companies with science based targets have performed better than a traditional market cap benchmark. And we can treat this in fact as a form of alpha as well. You can see some of these companies on the left hand side, the number of companies that are reporting these climate numbers. These ambitions for reducing carbon dioxide equivalents has been exponential over the past couple of years. Many of them are looking at outcomes well below two degrees, like the 1.5 degrees that are captured in the Paris aligned benchmark requirements. These SBTI companies have higher levels of profitability. They have also subsequently outperformed, partly because of that quality factor exposure. But I think also there's this additional emphasis on efficiency within these companies. You can do well at the same time as doing good, and we can bait that into our portfolios.

The same type of principles, reducing carbon, finding climate aware alphas can be done in all aspects of our portfolios. So I talked about this in equities, but we can go further and do this in fixed income, in sovereign, real and nominal bonds. We can do it also in real estate, in REITs that are publicly traded. We can even do it in commodities. We've done it for a full multiasset portfolio. Let me end by just talking briefly about how to do it for sovereign bond portfolios. There are some guidelines to help us create net zero or Paris aligned sovereign bond portfolios with the guidelines issued by the IIGCC. We've implemented this as an overlay and you can put this on any type of sovereign benchmark we want to do two things. We want to target a decarbonization, but we also don't want to deviate too much away from that sovereign benchmark. We can consider a direct allocation to green bonds, and finally, we can take higher positions in countries that are better aligned and more prepared for the transition to a net zero economy.

The IIGCC advocates that data from the climate change performance index should be used. That's called CCPI and it's actually produced by a nonprofit company called German Watch. There are several components in here. There are greenhouse gas emissions that now are computed for every country. We favor countries that have larger shares of renewable energy. We also want to more heavily weight countries that have climate policies more in focused and aligned with the net zero transition.

We've partnered with a company called Climate Trace. Climate Trace is a very interesting company and aims eventually to offer real time emissions tracking across the world. Using the Climate Change Performance Index, we can then obtain an overlay for bond portfolios. We then tilt into companies that have these more desired features, having lower emissions per capita, having more of its electricity or energy coming from sustainable means, countries with the right sort of government policies to help usher in this net zero transition. Here are some of these statistics for the CCPI data that we've used to help obtain climate overlays on our portfolios. One interesting thing is looking at the left hand side here, which is the amount of active risk on the x axis to the amount of improvements that you can make in carbon dioxide, greenhouse gases. And also the CCPI index itself, you can see that it starts to level off at about 2%, and that's actually the number one to 2% is what we target.

This is a number that still allows you to hew towards a benchmark, especially for sovereign bonds. That's very important to get diversification in our sovereign bond portfolios. But we can also at the same time, meet our climate ambitions and align for a Paris agreement low carbon future.

Okay, so I said we can do this across all portfolios, all asset classes. There's further information in some of the papers that have been mentioned throughout the talk. And I want to thank you for the opportunity to present this research to EDHEC back to Emmanuel. Thank you.

Emmanuel Jurczenko
Thank you very much, Andrew. Great presentation. Very, very interesting overview. So before I pass to my colleagues, first I just want to remind everybody that you can still post questions on the Q&A, right? So Laurent will manage them. And now it's my pleasure to pass it to Abraham. So, Abraham, the floor is yours now to discuss and react on Andrew presentation.

Abraham Lioui
Okay? So thank you very much, Andrew, for this super eyes opening presentation. And so we learn a lot, and I don't want to waste too much time. I prefer to let you speak and give us even more of your deep knowledge of this field. I would like to ask two questions please Andrew, the first one is related to the paper of Marcin so in fact, what is known in this literature, be it climate risk or ESG broadly is that there is a lack of consensus from the academia as to the existence or not of the alpha. So Marcin says the alpha goes from polluting to non polluting while you are saying the alpha goes from the non polluting to the polluting. My question is the following one we now know that there are two components to the alpha. One is the preference for climate ESG, et cetera? Which push people to renounce to part of the financial reward for the non-peconiary reward? So we expect highly performing firm to have a negative alpha from this channel. The second channel is the buzz around this that is, there is a positive shock to preferences and this positive shock will generate some positive realized returns.

So my question to you is the following one what do you think is the interplay between these two channels into the climate alpha world? Thank you.

Andrew Ang
Thank you. This is a great question Abraham. In fact, you've done some seminal work recently directly on this topic and I want to actually borrow your framework to think about this in the short term and in the long term. So one of the methods that you talked about was preference shops and those are absolutely seen in flows the whole sustainable space. We estimate it at about $1 trillion today and that's grown by four to five fold over the past three to five years. Been an absolute rapid change of investors entering sustainable funds. Now, what that will do in the short term is because, quote unquote, the old market adage of more buyers than sellers is if there is greater than demand, say from a preference shop, prices go up. In the long run, though, you have to have lower expected returns. Now, there are two components of the long run. The first component is a purely idiosyncratic component to sustainability or ESG. And most of our traditional theories would say that well, at least according to the CAPM, these are all idiosyncratic and therefore, because they can be diversified away, should not carry any systematic risk premium.

The second component is one that EDHEC researchers have worked on in a number of papers. And the second component is the systematic component which by definition in finance theory should be rewarded with higher returns for bearing systematic risk. We've shown in this presentation, as in other research as well, that sustainability has some significant factor exposure in particular to quality and minimum volatility. If those are systematic factors, which I believe and there are many papers along those lines in the literature, then that long term component will result in higher returns in excess of the market portfolio for bearing systematic style factor risk. So we've talked about the short run and the long run. Now, there's one other aspect which I think deserves bearing some comment on and that is ESG as a form of alpha, any alpha eventually will fade. Any alpha also requires skill. And here I think of the grossman sticklets type of approach to alpha where investors with greater access to information, with the resources to process that information and the ability then to implement it efficiently into portfolios. Well, all of those are hard and that might not be successful, but those are the ingredients required to generate alpha with ESG data.

And we aim to do all aspects of these. We look at flow information which might impact things in the short run. We've talked about the style factor exposure which gives us long-run expected returns and as this presentation has showed, we can use ESG or sustainable data to create alpha as well.

Abraham Lioui
Thank you very much Andrew. I have the second and my last question is related to a very nice property of what you talked about today is that you can reach alpha without divesting in a sector or in a company, which is fantastic. And my question is related to the transition risk because this way what you're saying is that we can reach some goal but without quote unquote punishing brown firms in the meantime. So I would like to hear you about this transition risk and how maybe asset managers and or regulators should help company transition because we will have no advantage to kill the Bronx company right now because unemployment, et cetera. So what's your take on the transition risk?

Andrew Ang
Yeah, I think there are three points here and I think the language is really well summarized by we want to navigate whatever that transition brings and at least just be prepared for potential greater risk. We want to also help drive the outcome to actually get us to a net zero future and be actually involved with companies and put those insights into portfolios that help bring about that net zero future sooner rather than later. And then I think thirdly, there is still so much that we have to do as a scientific community, not only in finance in our area, to put capital to those types of technologies and firms that will usher in the green revolution, but also in science to even actually have the ability to decarbonize, to have scalable and robust energy sources from renewables and also everything else that we can actually take out more and more carbon in our daily lives. So navigate, drive and then help invent, so navigate at a minimum I think is being prepared with risk. But I would actually go further to say that these Paris-aligned portfolios because they actually give us this outcome of 50% carbon reduction, 7% year on year to reach 2050, this helps drive capital into those companies which are in compliance with that net zero world.

Alpha is a really important component of this because alpha not only helps our portfolios, but it also helps capital go to those companies that achieve a double bottom line, an outcome of ESG, particularly net zero for the e in this case. And it also aids outperformance, and hence we call this the double bottom line, that we have financial performance as well as help to achieve an ESG outcome. And then finally, I think there might be a role here for impact, like more venture or private markets, potentially in a larger capacity than our traditional portfolios have done. And these give us a direct incentive to help foster this creativity and invention that's needed for the net zero future.

Abraham Lioui
Okay, thank you very much, Andrew, Emmanuel, I am done with my questions.

Emmanuel Jurczenko
Okay, thank you very much. So now I will let Laurent make an introduction of Etienne and I'll let you take the floor.

Laurent Deville
Thanks a lot, Andrew, for this very interesting presentation. Thanks a lot, Etienne, for accepting to kind of moderate the question and answers and to ask your own. So Andrew, just to say a few words, I'm only here as a go between. In between you, Andrew, and one of our amazing students, Etienne COLTA. And Etienne just well, is in his final year of education at EDHEC in the MSc in Climate Change and Sustainable Finance that we just created with Mines Paris Tech, a French engineering school. So our objective here in this program is to have a mix of the science of climate change and the finance of climate change. As you said in your presentation that what we offer in terms of solution is science based. I'm supposed to be the go between, but I'm still going to have a question for you because as a program director, you see we are seeing the evolutions in terms of market needs for students who have the capacity to accompany the financial industry within this big evolution of what they do.

And in your view, both from an academics but also from a practitioner, what would be the necessary knowledge for the students who will enter the industry to have an impact in terms of shifting the industry toward a greener world?

Andrew Ang
Well, I think there's both hard skills and soft skills and like Laurent and Abraham and yourself, my training is in financial economics, traditional training, and I actually had to pick up a fair bit of knowledge. Just hang on a second, I'll show you, son of my current reading. And here's another one. This book actually is very good if you want light reading or you suffer from insomnia like me. But it turns out that if you read these textbooks, they offer many of the same technical things that we're familiar with. There are numerical methods for solving ODEs and PDEs. They're green equations. We also have the same type of forward and backward equations. They're very, very similar. In fact, in some cases, the mathematics is actually quite straightforward because it's all newtonian, and it's in the language that we speak. But what I actually think is those things can be learned. We can also go to online courses for coding and for everything else. There's a huge amount of regulation and those things as well, you can pick up. But the number one thing that I think that is very hard to learn is how to bring together disparate fields into one.

And this, I think, is the key requirement is in order for us to get to net zero, it can't just be us making progress within each of our siloed scientific fields. It has to be that we need to bring much more cross-collaboration and field work across all the different disciplines together. It must be the case. Otherwise, how can we ever succeed in getting to net zero? And while we that are on this call, and you can see the faces of everyone here on this call, we're experts in a singular domain. I think we need to work, all of us, much, much harder to be experts at things across domain, and then we can make the necessary connections and allow us to get to a net zero future faster.

Laurent Deville
Okay, thank you very much, Andrew. I'm really reassured that we took this path and trying to mix the engineering perspective science of climate change with the finance of it. And Etienne, well, maybe now it's time to show how great our students are at EDHEC.

Etienne Colta
Thank you for giving me the floor. And thanks a lot Andrew, for your presentation. So I wouldn't consider myself an expert at all in physical engineering, but yeah, we have classes on that. So it's great to see both of you. So now I have some question regarding your presentation. And first, it's about the homogeneity of the ESG score. So there is several ESG data operators, so MSCI, FTSE, and the results are not correlated at all. And my first question is why did you choose MSCI? Sorry, data set in your presentation?

Andrew Ang
Yeah, that's a really good question. And like many other asset managers and data providers, we have our own proprietary ESG scores as well. Just a few comments on these. You're absolutely right that there is large heterogeneity, in fact, there's lack of homogeneity in ESG scores, data and rating methods. Roberto Rigobon at MIT has a very nice phrase. It's, in fact, the title of a paper called Aggregate Confusion on these different, very disparate approaches and underlying data. Now, that's actually the case for much of our data. In economics, there is no one standard source, even for prices. Stocks will even trade on different exchanges at different times. It's also not true for credit ratings or for data on probabilities of failure that we've also seen predominantly change the way that we've invested in fixed income over the last four or five decades. ESG is no different. I think, first of all, there are some data sets or standards that many people will use and we tend to want third party arm's length data for independence, verifiability and some form of transparency. So some cases makes sense to use a third party provider.

Also, I think that Heterogeneity is such a great thing. We will eventually standardize. There were dozens and dozens of different credit providers and now there are only three major credit providers in the world today. There are lots of local offerings, but there are three big global players. ESG probably will go along those same lines. There will be standardization. There is already a lot of consolidation in that industry. But I rue the day where everything will be standardized because one of the greatest opportunities that I feel with heterogeneous data is the opportunity to create alpha. The more aggregate confusion on here, the more opportunities there are for skilled investors to take an alternative data set, analyze it, and then efficiently implement it on portfolios. The same points that I talked about earlier, when everything becomes standardized, that alpha generation is going to be frankly a lot harder. And one of the reasons why I love using sustainable data for this great opportunity to create alpha is it probably is going to be around a little bit longer than the half lives of more standard alpha signals, which everybody really now has access to and uses. While sustainability isn't completely, adoption is not completely ubiquitous across society and across asset managers.

That represents better opportunities. And actually the half lives of these sustainable data signals are probably longer than the half lives of standard alpha signals.

Etienne Colta
Thanks for your answer. Sorry.

So now my next question is about the taxonomy. So you're not mentioning it in your presentation, but it's a huge topic in Europe with the EU taxonomy at the moment and does it mean it does not impact your portfolio and your investment strategy or just it's not a main focus right now?

Andrew Ang
Yeah, that's also a great question as well, touches upon regulation and definitions and as you say, how each individual asset manager wants to position their funds accordingly and to be in compliance with these new taxonomies. I want to answer the question this way, is that greater transparency is always good. It helps the end client have better decisions about where their money is going. It also helps better align incentives between the asset manager and the end owner of that capital. And lastly, it's also good for the whole marketplace to kind of have these agreements because that standardization then helps in an aggregate level, helps regulators and helps society really know what's under the hood and what this money does and where it's going. So overall I think all of those things along the EU taxonomy lines, these are really good for the investments business and for the investors themselves. There are some concerns in all of this. Greenwashing, for example. In fact, we have a signal that explicitly looks at greenwashing and measures ESG controversies. There might be some cases. There have been some cases for asset managers, one very large one in fact, that really haven't done what they say.

And so there's truth in labeling behind this. And so there's still some monitoring that regulators have to do and it hasn't been completely ingrained or integrated, ESG integrated into all of the investment processes at these firms. I think there's also a concern for mislabeling, right? And so that's a little bit like having a fund name not reflect the underlying positions or strategies of what that fund is doing. That's not a new problem, but it is certainly very important as more and more capital gets into these sustainable funds. And then finally, I think there's also a public good externality impact here. What will happen to all these non compliant funds, particularly SFDR 809, and then consequences for potential assets that might be stranded or at least maybe have severe price declines. And we've seen some of those things, I think, play out with unintended play out with very wild energy prices, especially in Europe this year. And I do think some of those things are related to where capital has gone and where the investments have been made especially or actually not made in some cases. And that has had consequences for all of business and society.

Etienne Colta
Thanks a lot. So another question now is about the decarbonization strategy. So it's based on the carbon intensity. And my question is, why don't you base it on the brute emission, as the impact is not the same on the carbon budget?

Andrew Ang
Yeah, that's a great question and it's one that's really deep in the weeds. Carbon emission intensities are defined quite differently and I think it really depends on your purpose. So for a Paris aligned benchmark by the EU Tech and the IIGCC, they define carbon emissions as scope one and two. Later on in a couple of years, it will be scope three as well, divided by enterprise value. Now, there are other definitions. You might divide that by sales or you might divide that by revenue. And these all have, I think, different purposes. I think at the end of the day, the EU Tech guidelines and the IIGCC for Paris alignment, ultimately enterprise value, that's what you have for a whole company's worth, and it makes sense for that. Now, the other things about the intensity is concerns on the numerator. We just talked about different denominators in this. Should it be scope one, which is directly under a company's control? Well, you kind of have a little bit more control, but not complete control. Depending on the location that you are in and the type of energy that you're using in your business, you might have less control over scope two and scope three all the way down your suppliers. As well, it's sometimes very hard to even know what those suppliers are to identify them. And then you have to actually influence some suppliers or up and down the supply chain. That in itself is also a separate issue. And then there's the issue of double counting for scope two and three as well.

Now, all of this, I think we can make some sensible choices which have been done in adopting strategies that meet the Paris agreement criteria or that fulfill the requirements of a Paris aligned benchmark. But they're really, really good questions. We use different formulations for Alpha. We use different formulations when we estimate these along the supply chain. I think each of them has a purpose in there and some of it's regulatory, some of it's direct for investments and reporting and definitions will be appropriate for Alpha generation.

Etienne Colta
Thanks a lot. And last question from my side, you talked also about science based targets. And my question is how BlackRock support and assist companies are achieving their targets if you have special support.

Andrew Ang
Well, our company, BlackRock, our CEO, Larry Fink, has been quoted extensively and written major pieces about the importance of the climate transition. He has directly said that climate risk is investment risk. It also represents tremendous opportunities for investors as well. A crucial part of navigate, drive and invent to get to this net zero future, I think, is stewardship. And our firm has taken several steps to help improve our engagement with companies and to encourage all companies to take into account these science based targets and other things, many other things, to help achieve the net zero future. In fact, it really can't be done by ourselves. It needs the whole of society, the whole of finance and stewardship activities are an integral component of that.

Etienne Colta
Thanks a lot. And now I'm moving on to the Q&A question from today and the people following the discussion. So what progress has BlackRock made on integrating ESG or climate metrics into factorial trackers?

Andrew Ang
Yeah, 100% of all funds that are managed at BlackRock, including the index implementations and obviously our large active business, 100% of all BlackRock funds have integrated ESG into our investment process in factors. And that's the business I'm most associated with. We have launched series of funds, I mentioned these, that we integrate ESG and factor premier directly. We have also used these ESG data and signals in the factor definitions themselves. And I talked about green patents and corporate culture as two great examples of using ESG data to enhance the factor definitions. If you look at our reports, if you go to our websites, then every fund that's listed there will have information that pertains to climate and other ESG data. So those are reported publicly for all funds that we manage.

Etienne Colta
Okay, thank you. And then another question is, do you think decarbonization is happening quickly enough? And how can BlackRock accelerate progress within sustainability tilted portfolios?

Andrew Ang
I think we should try to achieve net zero as fast as we can. It's not only us as in BlackRock, it involves efforts from individuals, myself and you, from governments, from banks, and BlackRock is an asset manager. We have a valuable role to play too. And the corporations and societal norms, all of these are important. And ideally, everything operates together. Governments set the right policy, companies act voluntarily as well as in compliance with changing laws. And then we get more capital into areas where we can make a bigger difference and usher in a green economy faster. It's going to require everything. Do I think the decarbonization is happening fast enough? I think all of us would wish it could happen instantaneously and we should all, I think, have a goal to make it happen as quickly as we can.

Laurent Deville
Okay, if I can jump in. A few weeks ago we received Frederic Samama, one of the authors of The Clean Swan. I think you're aware of what they do. And I think one of their last results with Patrick Bolton is the fact that the more we wait, the harder it will be. And so I think this is related to what you're saying. And I think one of his concerns was that not the where we go. And you really clearly stated where we need to go. And everyone knows, but how do we do, how do we get there? And I think that their perspective was more on the carbon budget. And that was related to one of the questions that was asked by the audience as well. Should we work with the carbon budget and say, well, we cannot go beyond that carbon budget and no matter how, we have to cut companies, in a sense from our portfolios so that we can keep this carbon budget at the limit that is set by whom? This is an open question, but that would be set by science.

Andrew Ang
In a sense, the Paris aligned criteria for all the things that I said about a 50% reduction in carbon off the bat and then 7% decreases in carbon year on year are in effect a carbon budget. You've just actually stated this in a limit sense. Rather than having a number that you fill up to, it's going the opposite way of here's. The current carbon emissions, let's reduce it to a particular number. But in effect, they're kind of like a glass half empty and a glass half full. It's the same thing. I think the most interesting question here is not so much this, but it's about the whole portfolio. So what I presented was you can take of these carbon reduction in equities, which we showed directly. You can do it in sovereign bonds, which I also showed you can do it in a real estate portfolio. There is still a lot of work to be done to have our privates portfolio decarbonized too. BlackRock has some partnerships there for decarbonization, partners for direct investments, but the whole private markets industry, there is still quite a lot of work to do. To me, the really big question is not so much each individual asset class, how do you get the whole portfolio to be sustainability aware?

Questions like if I do have this carbon budget or carbon limit, should it be more aggressive in public markets than private? Should we actually be achieving carbon more aggressively in equities and less so in our sovereign bond portfolios? We can actually choose which companies to hold there but often investors have requirements to hold certain types of government bonds particularly for regulated industries like insurance and many large investors actually need that security or safety reasons for government bonds which are a little bit different. What about direct investments? Should I actually take more carbon out from a public market portfolio so that I might be actually able to buy brown assets and make them green? Should I actually have or what size should I have for explicit allocations to small impact equities, particularly in these types of technologies or to green bots and where would actually that budget be transferred across those asset classes? Those are really difficult questions and I think those are questions that we don't know the answers to right now but we will need to and I think work from you at EDHEC and everyone in the industry. These are some of the questions that we will need to answer over the next couple of years.

Emmanuel Jurczenko
Thank you Andrew, related to what you are talking about, do you have some elements with respect to the asset allocation? Right?

Because this is what you are talking about that means now we have to take into account the climate impact with respect to economy. So I would be interesting to have your views about the impact you see with respect to growth, with respect to inflation, with respect to countries and what is your reflection with respect to how to adapt the strategic asset allocation. So it's bit related to what you are talking about and I think this is obviously an important topic for any asset allocator that means how you can factoring in fact the climate either physical or transition risk impact. So relation and I know that you work a lot on the interconnection between economy and financial markets so how do you see in fact the change that climate will bring with respect to asset classes, behavior and the link with the economy?

Andrew Ang
Well this is the question Emmanuel that we will have to work on this together but I think we can say a few things so far. So the first is I think we have to be invested in all sectors and a strict divestment approach is not optimal. We want the whole of society to decarbonize not just take their easy road out by divesting from a few sectors. All of society has to move. And second we've actually seen over the last year major impacts by not holding a sector like energy and that's really affected some investor returns if they did not hold that sector in their portfolios. So I completely agree with the paris align benchmark requirements that we have to do this in a sector balanced or sector neutral way across all sectors. Now, what's interesting is if we look at the bond market, the corporate bond market now that money that's invested in the corporate bond market actually gets returned to us periodically when these bonds mature. And bonds are often issued for investments in specific activities, sometimes tied to specific projects. Indeed, some bonds actually have as collateral these actual projects for which the money is being raised.

But because the bonds mature, there's money that's recycled and coming to work all the time. And it's for that reason that we might not want to have. And in fact, the Paris aligned benchmark requirements do not specify any sector requirements on a corporate bond portfolio. We don't want to be tethered to where capital has gone. We want to have the opportunities to put money to work in corporate bonds for where that future is going.

Equity is different because that represents permanent capital. Now, taken further, that means there might be a role for dedicated allocations to green bonds which we build into our portfolio. But what about dedicated allocations to impact like funds or to funds specifically earmarked to raise capital for the green revolution? Perhaps those also might serve a purpose too. Another one is often we see private markets that play special roles in directing capital. Maybe that translates into treating private markets investments in this new green portfolio strategic allocation a little differently than the approach that we've taken in the more traditional asset allocation approaches. At BlackRock, we've built capital and market assumptions. So these are expected return assumptions. They change periodically and we have embedded climate data scenarios and other considerations associated with physical risk and transition risk.

We have built those into capital market assumptions for the asset classes. Other investment managers have also done the same. But I think once we have that portfolio construction along the principles that I just outlined, combined now with capital market assumptions that reflect going into the future, that has to consider climate those two now, we can then think about building a robust future-proof climate portfolio.

Emmanuel Jurczenko
OK, thank you. Thank you very much, Andrew. I think we are close to the end. So I first want to thank you very much for sharing your research for this great presentation. On behalf of EDHEC, I want to thanks all the students and all the people that attending to this first session. There are several people I want to thank which are part of EDHEC business school, so Emilie from the Finance graduate programs, there is Christophe from IT department, Barbara Bredaut from Communication department that help us in fact to set up this first session. And obviously, I want to thank Abraham, I want to thank Laurent and I want to thank Etienne for their participation to this first session. So, as I said, as a reminder, you will be able, in fact, to find we have a dedicated webpage for this series. You will be able to find the slides of Andrew. And I want also to take the opportunity to tell you that the next session is already planned. It will be on Thursday, 17 February. And this will be in fact, we will continue this journey. There will be a presentation done by Eric Bouyé.

Eric Bouyé is head of research at World Bank and he will talk about Sovereign Sustainable Finance. So we'll talk about, in fact the role and the practice of multilateral development banks for financing sustainable development. So I think we are done today. Again, great thank you, Andrew. Thank you for your time and thank you for the quality of your research. And there are a lot of stuff to do and we hope to be able to contribute as a community to the decarbonization of our society. Thank you very much. See you soon.

Laurent Deville
Good evening.

Emmanuel Jurczenko
Bye bye.

Abraham Lioui
Thank you, Andrew.

Etienne Colta
Bye bye. Thanks.

Make an impact
EDHEC BUSINESS SCHOOL
[Music]