We analyze the joint out-of-sample predictive ability of a comprehensive set of 299 firm characteristics for cross-sectional stock returns. We develop a cross-sectional out-of-sample R2 statistic that provides an informative measure of the accuracy of cross-sectional return forecasts in terms of...

In this paper we address three main objections of behavioral finance to the theory of rational finance, considered as “anomalies” the theory of rational finance cannot explain: (i) Predictability of asset returns; (ii) The Equity Premium; (iii) The Volatility Puzzle. We offer resolutions of...

I use classification-based machine-learning methods to decompose 32 anomaly payoffsinto risk exposures and mispricing. The component driven by risk earns statistically insignificantreturns, despite its efficacy in explaining the time-series variation in anomaly payoffs.The mispricing component...

We conduct a comprehensive asset pricing analysis for the U.S. property/liability insurance industry using monthly data from 1988 to 2015. We find that state-of-the-art models such as the Fama and French (2015) five-factor model cannot explain the returns of property/liability insurance stocks...

This paper studies the wealth and pricing implications of loss aversion in the presence of arbitrageurs with Epstein-Zin preferences. Loss aversion affects an investor's survival prospects mainly through its effect on the investor's portfolio holdings. Loss-averse investors will be driven out of...

We study the effect of economic uncertainty exposure (EUE) on cross-sectional return differentiating the mispricing from ambiguity-premium effects. Conditional on a common mispricing index, we find that EUE induces disagreement which amplifies mispricing. The highest EUE quintile produces an...

To confront the challenge that disaster risk is “dark matter” in finance, we construct an objective measure of disaster risk, which is able to predict half of GDP crashes in a sample of 20 advanced economies between 1870 and 2021. Despite this significant predictability, we find no...

We analyze the announcement risk premia on the US market between September 1987 and March 2023 and find that the market index exhibits average excess returns of 8.3 bps for macroeconomic announcement days. This strongly contrasts with 1.4 bps returns for non-announcement days. We further measure...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...