Cross-sectional expected returns: new Fama–MacBeth regressions in the era of machine learning

Yufeng Han, Ai He, David E Rapach and Guofu Zhou

Review of Finance, 2024, vol. 28, issue 6, 1807-1831

Abstract: We extend the Fama–MacBeth regression framework for cross-sectional return prediction to incorporate big data and machine learning. Our extension involves a three-step procedure for generating return forecasts based on Fama–MacBeth regressions with regularization and predictor selection as well as forecast combination and encompassing. As a by-product, it provides estimates of characteristic payoffs. We also develop three performance measures for assessing cross-sectional return forecasts, including a generalization of the popular time-series out-of-sample R2 statistic to the cross section. Applying our extension to over 200 firm characteristics, our cross-sectional return forecasts significantly improve out-of-sample predictive accuracy and provide substantial economic value to investors. Overall, our results suggest that a relatively large number of characteristics matter for determining cross-sectional expected returns. Our new method is straightforward to implement and interpret, and it performs well in our application.

Keywords: penalized regression; forecast combination; forecast encompassing; characteristic payoff; cross-sectional out-of-sample R2 statistic (search for similar items in EconPapers)
JEL-codes: C21 C45 C53 C55 C58 G12 G17 (search for similar items in EconPapers)
Date: 2024
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