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Journal Article

Fact, Fiction, and Factor Investing: Practical Applications

This piece distills the central concepts and practical takeaways of our Fact, Fiction, and Factor Investing article, which examined many claims about factor investing, referencing an extensive academic literature and performing simple, yet powerful, analysis to address those claims.

Journal Article

Fact, Fiction, and Factor Investing

Factor investing has been around for several decades, backed by an enormous body of literature, and yet it is still surrounded by much confusion and debate. We examine many of the claims about factor investing, referencing the academic literature and performing simple, yet powerful, analysis to address them.

Working Paper

Pricing Without Mispricing

We offer a novel test of whether an asset pricing model describes expected returns in the absence of mispricing. Our test assumes such a model assigns zero alpha to investment strategies using decade-old information. Prominent multifactor models do not satisfy this condition – while multifactor betas help capture current expected returns on mispriced stocks, persistence in those betas distorts the stocks' implied expected returns after prices correct.

Working Paper

What Can Betting Markets Tell Us About Investor Preferences and Beliefs? Implications for Low Risk Anomalies

We relate the low risk anomaly in financial markets to the Favorite-Longshot Bias in betting markets and provide novel evidence to both anomalies. Synthesizing the evidence, we study the joint implications from the two settings for a unifying explanation. Rational theories of risk-averse investors with homogeneous beliefs cannot explain the cross-sectional relationship between diversifiable risk and return in betting markets. Rather, we appeal to models of non-traditional preferences or heterogeneous beliefs.

Working Paper

What Can Betting Markets Tell Us About Investor Preferences and Beliefs? Implications for Low Risk Anomalies

We relate the low risk anomaly in financial markets to the Favorite-Longshot Bias in betting markets and provide novel evidence to both anomalies. Synthesizing the evidence, we study the joint implications from the two settings for a unifying explanation. Rational theories of risk-averse investors with homogeneous beliefs cannot explain the cross-sectional relationship between diversifiable risk and return in betting markets. Rather, we appeal to models of non-traditional preferences or heterogeneous beliefs.

Working Paper

Understanding Momentum and Reversals

Stock momentum, long-term reversal, and other past return characteristics that predict future returns also predict future realized betas, suggesting these characteristics capture time-varying risk compensation.

Journal Article

Value and Interest Rates: Are Rates to Blame for Value’s Torments?

Some have blamed the interest rate environment for value stocks’ underperformance of growth stocks from 2017 to early 2020, as well as the stretch of lackluster performance for some value factors since Global Financial Crisis. We find the performance of value is not easily assessed based on the interest rate environment, and that factor timing strategies based on interest rate-related signals are likely to perform poorly.

Working Paper

Beyond Basis Basics: Leverage Demand and Deviations from the Law of One Price

Bases are driven by intermediaries’ cost of capital and the amount of leverage demand for an asset. Focusing on leverage demand, we find bases negatively predict futures and spot market returns with the same sign in both global equities and currencies.

Journal Article

How Do Factor Premia Vary Over Time? A Century of Evidence

We examine four prominent factor premia – value, momentum, carry, and defensive – over a century from six asset classes. The results offer support for time-varying risk premia models with important implications for theory seeking to explain the sources of factor returns.

Journal Article

Can Machines "Learn" Finance?

Can Machines “Learn” Finance?” was named the winner of the 2020 Harry M. Markowitz Award. Machine learning for asset management faces a unique set of challenges that differ markedly from other domains where machine learning has excelled. We discuss a variety of beneficial use cases and potential pitfalls for machine learning in asset management, and emphasize the importance of economic theory and human expertise for achieving success through financial machine learning.