Do Anomalies Really Predict Market Returns? New Data and New Evidence

Cakici, Nusret; Fieberg, Christian; Metko, Daniel; Zaremba, Adam

Do Anomalies Really Predict Market Returns? New Data and New Evidence

Abstract

Using new data from U.S. and global markets, we revisit market risk premium predictability by equity anomalies. We apply a repertoire of machine learning methods to 42 countries to reach a simple conclusion: anomalies, as such, cannot predict aggregate market returns. Any ostensible evidence from the U.S. lacks external validity in two ways: it cannot be extended internationally and does not hold for alternative anomaly sets—regardless of the selection and design of factor strategies. The predictability—if any—originates from a handful of specific anomalies and depends heavily on seemingly minor methodological choices. Overall, our results challenge the view that anomalies as a group contain helpful information for forecasting mar-ket risk premia.

Keywords

equity anomalies return predictability machine learning international stock markets equity premium

Citation

Nusret Cakici, Christian Fieberg, Daniel Metko, Adam Zaremba, Do Anomalies Really Predict Market Returns? New Data and New Evidence, Review of Finance, Volume 28, Issue 1, January 2024, Pages 1–44, https://doi.org/10.1093/rof/rfad025

URI

https://open.icm.edu.pl/handle/123456789/24911

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