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 market risk premia.