Cross-Sectional Expected Returns: New Fama-MacBeth Regressions in the Era of Machine Learning
Review of Finance, forthcoming
41 Pages Posted: 13 Jun 2018 Last revised: 30 Apr 2024
Date Written: August 07, 2024
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 byproduct, 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 R-squared 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 R-squared statistic
JEL Classification: C21, C45, C53, C55, C58, G12, G17
Suggested Citation: Suggested Citation
, Available at SSRN: https://ssrn.com/abstract=3185335 or http://dx.doi.org/10.2139/ssrn.3185335