Research
Publications
Publications
with Bryan Kelly and Lasse H. Pedersen
Journal of Finance,
Winner of the Dimensional Fund Advisor Prize distinguished paper
Code: Learn how to work with global return and accounting data and how to use empirical Bayes to estimate a hierarchical model of global equity factor returns
Data: Stock returns, firm characteristics, and equity factors from 93 different countries
Abstract:Several papers argue that financial economics faces a replication crisis because the majority of studies cannot be replicated or are the result of multiple testing of too many factors. We develop and estimate a Bayesian model of factor replication, which leads to different conclusions. The majority of asset pricing factors: (1) can be replicated, (2) can be clustered into 13 themes, the majority of which are significant parts of the tangency portfolio, (3) work out of sample in a new large data set covering 93 countries, and (4) have evidence that is strengthened (not weakened) by the large number of observed factors.
Working Papers
Working Papers
with Marc Eskildsen, Markus Ibert, and Lasse H. Pedersen
Abstract:The greenium (the expected return of green securities relative to brown) is a central impact measure for ESG investors. Replicating the literature’s wide range of greenium estimates based on realized returns, we find that these are not robust to changing the greenness measure or time period. Instead, we propose a robust green score combined with forward-looking expected returns, yielding a more precisely estimated annual equity greenium of -25 basis points per standard deviation increase in greenness. The greenium is more negative in greener countries and over time. Finally, we provide greeniums for corporate bonds, weighted-average costs of capital, and sovereign bonds.
Abstract:
I use subjective risk and return expectations to provide a novel perspective on the risk-return tradeoff in the stock market. I show that the relationship between subjective risk and realized returns is weak, but that the relationship between subjective risk and subjective expected returns is strong. Consistent with these patterns, subjectively risky stocks appear overvalued, as they have overoptimistic cash flow expectations, negative earnings announcement returns, and abnormally high short-selling demand. The empirical findings cannot be explained by leading models of the risk-return tradeoff, but they can be explained by a model where some investors have an optimism bias.
I use subjective risk and return expectations to provide a novel perspective on the risk-return tradeoff in the stock market. I show that the relationship between subjective risk and realized returns is weak, but that the relationship between subjective risk and subjective expected returns is strong. Consistent with these patterns, subjectively risky stocks appear overvalued, as they have overoptimistic cash flow expectations, negative earnings announcement returns, and abnormally high short-selling demand. The empirical findings cannot be explained by leading models of the risk-return tradeoff, but they can be explained by a model where some investors have an optimism bias.
with Bryan Kelly, Semyon Malamud, and Lasse H. Pedersen
R&R, Review of Financial Studies
Abstract:We propose that investment strategies should be evaluated based on their net-of-trading-cost return for each level of risk, which we term the "implementable efficient frontier." While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading costs leads to excessive reliance on fleeting small-scale characteristics, resulting in poor net returns. We develop a framework that produces a superior frontier by integrating trading-cost-aware portfolio optimization with machine learning. The superior net-of-cost performance is achieved by learning directly about portfolio weights using an economic objective. Further, our model gives rise to a new measure of "economic feature importance."