In response to your first question, yes I do have interests in multiple research areas. Specifically, I conduct research in empirical asset pricing and financial econometrics. My research in asset pricing focuses on time-series and cross-sectional return predictability in the large sample of U.S and international equities, corporate bonds, options, and hedge funds. My research also focuses on derivative pricing, modeling time-varying return distributions, and financial risk management. In terms of methods, my work in asset pricing utilizes both economic models and econometric techniques to address the research questions raised in the papers. Not only do I use the economic models in my research to generate testable predictions that I empirically analyze in the data, but I also provide behavioral explanations to cross-sectional return patterns. A common feature of my research is the use of big data (such as millions of equity and corporate bond return observations).
In response to your second question, I’d like to share my key findings from my recent publication in the Journal of Financial Economics (JFE). Over the past three decades, financial economists have identified a large number of risk factors that explain the cross-sectional variation in stock returns. In contrast, far fewer studies are devoted to the cross-section[JF1] of corporate bond returns. Compared to the size of the U.S. equity market ($19 trillion), the corporate bond market is relatively small with a total amount outstanding of $12 trillion. However, the issuance of corporate bonds is at a much larger scale than the issuance of stocks for U.S. corporations: an annual average of $1.3 trillion for corporate bonds compared to $265 billion for stocks since 2010. Moreover, corporate bonds play an increasingly important role in institutional investors’ portfolios, evidenced by the recent influx to bond funds. Both corporate bonds and stocks are important financing channels for corporations, and both are important assets under management for fund managers. Thus, it is pivotal to enhance our understanding of the common risk factors that determine the cross-sectional differences in corporate bond returns. In my JFE 2019 article (co-authored with Jennie Bai and Quan Wen) “Common Risk Factors in the Cross-Section of Corporate Bond Returns”, we investigate the cross-sectional determinants of corporate bond returns and find that downside risk is the strongest predictor of future bond returns. We also introduce common risk factors based on the prevalent risk characteristics of corporate bonds – downside risk, credit risk, and liquidity risk – and find that these novel bond factors have economically and statistically significant risk premia that cannot be explained by long-established stock and bond market factors. We also show that the newly proposed risk factors outperform all other models considered in the literature in explaining the returns of the industry- and size/maturity-sorted portfolios of corporate bonds.
The objective of this course is to provide essential economic/econometrics background in asset pricing and to discuss the most important results in the empirical asset pricing literature. First, we reviewed the standard asset pricing models such as the mean-variance portfolio theory, the capital asset pricing model (CAPM), the conditional CAPM, the intertemporal CAPM (ICAPM), the conditional ICAPM, and the three-moment asset pricing models with skewness preference. Second, we studied key empirical methods and econometric techniques used in the empirical asset pricing literature; such as the univariate and bivariate long-short portfolio formation, risk factor construction, Fama-MacBeth cross-sectional regressions, univariate and multivariate GARCH models, correcting errors in variables, and the alpha tests. Third, we examined equity market anomalies, such as the size and value premium, momentum, short-term and long-term reversals, liquidity, idiosyncratic volatility, skewness, and lottery demand. We also discussed rational and behavioral explanations to these well-known equity market anomalies. Finally, we discussed the new findings and key factors in the sample of optionable stocks, such as the volatility risk premia, call-put implied volatility spread, and implied volatility innovations.
Let me first say that I had a lot of fun teaching in London and I found the students in my asset pricing course to be exceptionally motivated and talented. I have seen many PhD students even with Wall Street experience struggle with this course, being that it is a quantitative subject and requires a wealth of practical experience, but the students at EDHEC felt right at home with their strong math/stat background and industry experience. I could tell from the questions they posed in class that their interests ran deeper. The biggest challenge for the traditional PhD students is to come up with new ideas with practical implications, whereas the EDHEC PhD students typically have many ideas originating from their work and their challenge is finding time to execute them. I interacted with many doctoral students at well-known business schools in the U.S., and the performance of EDHEC PhD students ranks with good students I have seen at these prestigious centers for doctoral training in finance.
Based on my personal experience and serving on editorial boards, I find that empirical work with asset pricing and/or risk management implications has the best chance of having impact and being published. I encourage students to read almost all published and working papers in their own research domain to understand and explain their marginal contribution to the review team (editors, associate editors, and referees). I also encourage them to read something other than academic research, such as the Financial Times and the Economist, to develop new ideas with important practical implications. Another recommendation is to minimize distraction and maximize efficiency and productivity, since the key to success in publishing is to work hard!
If they are interested in empirical asset pricing, they can investigate time-series and cross-sectional return predictability patterns in the sample of optionable stocks that are significantly different from the entire stock sample. I recommend that they learn and implement new techniques currently used in the machine learning literature because the empirical asset pricing literature may further evolve in Bigdata/FinTech and machine learning. A large number of studies since the 1980s investigate the cross-sectional variation in equity returns, whereas far less studies are devoted to the cross section of corporate bond returns. Thus, I suggest that they come up with new ideas relevant to institutional investors (pension/mutual funds and insurance companies) trading in the corporate bond market. If they focus on the equity literature, I suggest that they try to come up with rational or behavioral explanations to puzzling empirical findings, such as the credit risk puzzle, the distress risk puzzle, the low-risk anomalies, and the lottery demand phenomenon.