What were EDHEC-risk’s top 10 most read articles in 2020?
A Year in Research: focus on retirement investing, sustainable investing, factor investing, and machine learning
As we enter a new year, EDHEC-Risk Institute takes a look back at the most read articles in 2020, covering a diverse range of topics that are at the heart of its expertise.
#1 Introducing "Flexicure" Retirement Solutions
A major crisis is threatening the sustainability of pension systems across the globe. In response to these concerns, a number of so-called retirement products have been proposed by insurance companies and asset management firms. But individuals are currently left with an unsatisfactory dilemma between on the one hand insurance products that provide security but lack flexibility, and on the other hand investment products that provide flexibility but no security with respect to the level of future replacement income. Fortunately, existing financial engineering techniques can be used to design new forms of “flexicure” investment solutions that can offer individuals both security and flexibility when approaching retirement investment decisions, thus providing a way out of the impasse of a choice between annuities and target date funds.
#2 EDHEC European ETF, Smart Beta and Factor Investing Survey 2020
The latest edition of the EDHEC European ETF, Smart Beta and Factor Investing Survey was conducted as part of the "ETF, Indexing and Smart Beta Investment Strategies" research chair at EDHEC-Risk Institute, in partnership with Amundi.
With this survey, we aim to provide insights into investor perceptions of exchange-traded funds (ETFs) and of smart beta and factor investing strategies, building on the analysis of this year’s responses and relating them to past results of our annual survey.
In 2020, the survey results show a slowdown in the use of smart beta and factor investing strategies, and a growing interest for the integration of an ESG component into investment.
#3 How Factor Investing Can Help Liability-Driven Investors
Investment practices in institutional asset management have been profoundly impacted by the rise of two new paradigms: factor investing and liability-driven investing. Interestingly, both of these approaches have conceptual justifications in financial theory, an encouraging sign that the famous “gap between theory and practice” is not as wide as it once appeared. The recent study published by EDHEC-Risk Institute with the support of Amundi as part of the “ETF, Indexing and Smart Beta Investment Strategies” research chair aims to establish a series of connections, between factor investing and liability-driven investing.
The recent work conducted at EDHEC-Risk Institute shows that liability-driven investors will benefit from adopting a factor perspective for the construction of their building blocks and the allocation to these components.
#4 Scientific Beta Low Carbon Option
Faced with the climate emergency, it is conservative to assume that there will be growing ecological, socio-political, and economic pressure on governments to set and enforce climate policies that materially reduce the gap between current emissions and the levels required to mitigate climate change. For most companies, this political response is a major component of the risks of a transition to a low carbon economy, which also include the impact from evolving technology, social norms and consumer behaviour. These risks could materially affect the financial positions of companies through balance sheet and income statement effects.
Thus, while ethical and socially responsible investors should be expected to orient their investments, engagement and outreach policies to contribute to the fight against climate change, all investors need to consider the possible financial impacts of climate change on their portfolios.
Against this backdrop, Scientific Beta is introducing a Low Carbon fiduciary option that is applicable across its entire flagship offering of multi-factor indices. It addresses the three most common decarbonisation objectives for investors:
1. Contributing to the transition to a low carbon economy;
2. Reducing the “carbon footprint” of investments;
3. Reducing exposure to climate change risks.
#5 Climate Change Finance : The Big Picture
The transition towards a low-carbon economy will require profound innovations in the way the global financial system manages climate-related risks.
The initiatives to enhance the transparency of climate exposures of banks and asset managers are only the first step in the process of making the financial system resilient to climate risks.
However, financial markets do appear to lack the tools and instruments needed by investors and financial intermediaries to effectively deal with climate risks.
Policymakers should create the conditions to facilitate climate–related financial innovations.
#6 Risk Optimizations on Basis Portfolios: The Role of Sorting
This paper investigates the mean-variance and diversification properties of risk-based strategies performed on style or basis portfolios. Authors show that the performance of these risk strategies is improved when performed on portfolios sorted on characteristics correlated with returns and is highly sensitive to the sorting procedure used to form the basis assets.
Whereas the extant literature provides mixed support for the outperformance of smart beta strategies based on scientific diversification, our designed strategies outperform both the market model and multifactor model.
Their testing framework is based on bootstrapped mean-variance spanning tests and shows valid conclusions when controlling for multiple testing, transaction costs, and luck from random basis portfolio construction rules. Economically, their results are supported by diversification-based properties.
#7 Robust and interpretable Liquidity Proxies Market and Funding Liquidity
He introduces a method to create two interpretable liquidity measures, which we associate with market and funding liquidity. The construction is based on creating two parsimonious linear combinations of the many liquidity proxies often used in the liquidity literature, both displaying mean-reverting behaviour, but characterized by very different reversion speeds. His construction does not require transaction-level data (such as volume or bid-offer spreads), but correlates well both with other measure that do, and with other liquidity proxies (liquidity as ‘noise’, liquidity as broker-dealer leverage) recently introduced in the literature.
#8 Machine Learning for Investment Decisions: A Brief Guided Tour
Recent developments in data science and machine learning have the potential to improve investment decisions. The fast growth of machine learning algorithms has occurred along with the expanding availability of data at the micro-level.
These data hold the key to new breakthroughs. On the other side, there are several challenges to full implementation in investing. One of these is the evolving nature of the investment landscape, where new products and services arise and quickly become widely available and possibly reducing future performance.
A potential example is factor investing. Privacy and security are continuing concerns.
He reviews a few curated applications and speculate on the impacts on finance broadly.
#9 Covid 19 and Smart Beta
The arrival of COVID-19 created significant stress in equity markets and led to considerable underperformance of smart-beta products that often have been sold to investors as cheap alpha as noted in Amenc (2016).
They document smart-beta and ESG returns during different stages of the still ongoing crisis and relate them to the underlying (unprecedented) industry rotation induced by the COVID-19 pandemic.
The objective of the paper is to find evidence for our conjecture that smart-beta returns have been heavily impacted by a crisis specific industry rotation, that can not be generalized. Relative winners (loser) might just have been coincidentally exposed to the right (wrong) sectors.
#10 Measuring Volatility Pumping Benefits in Equity Markets
It has been argued that portfolio rebalancing, defined as the simple act of resetting portfolio weights back to the original weights, can be a source of additional performance.
This additional performance is known as the rebalancing premium, also sometimes referred as the volatility pumping effect or diversification bonus since volatility and diversification turn out to be key components of the rebalancing premium.
The rebalancing premium, intrinsically linked to long-term investing, is typically defined as the difference between the expected growth rate of a rebalancing strategy and the expected growth rate of the corresponding buy-and-hold strategy, where the portfolio growth rate is the compounded geometric mean return of the portfolio, a meaningful measure of performance in a multi-period setting.
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