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Review of Finance Vol. 28 No. 6 2024

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

Yufeng Han1; Ai He2; David E. Rapach3; Guofu Zhou4

1 Belk College of Business, University of North Carolina at Charlotte, Charlotte, North Carolina, · 2 Darla Moore School of Business, University of South Carolina, Columbia, South Carolina, · 3 Federal Reserve Bank of Atlanta, Atlanta, Georgia, · 4 Olin Business School, Washington University in St Louis, St. Louis, Missouri,

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.

DOI
10.1093/rof/rfae027
Volume
28
Issue
6
Pages
1807-1831
Language
en
Sources
bibtex:phds-export.bib openalex crossref

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