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

See all articles by Yufeng Han

Yufeng Han

University of North Carolina (UNC) at Charlotte - Finance

Ai He

University of South Carolina - Darla Moore School of Business

David Rapach

Research Department, Federal Reserve Bank of Atlanta; Washington University in St. Louis

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School

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

Han, Yufeng and He, Ai and Rapach, David and Zhou, Guofu, Cross-Sectional Expected Returns: New Fama-MacBeth Regressions in the Era of Machine Learning (August 07, 2024). Review of Finance, forthcoming
, Available at SSRN: https://ssrn.com/abstract=3185335 or http://dx.doi.org/10.2139/ssrn.3185335

Yufeng Han

University of North Carolina (UNC) at Charlotte - Finance ( email )

9201 University City Boulevard
Charlotte, NC 28223
United States

Ai He

University of South Carolina - Darla Moore School of Business ( email )

1014 Greene Street
Columbia, SC 29208
United States

HOME PAGE: http://www.aihefinance.com/

David Rapach

Research Department, Federal Reserve Bank of Atlanta ( email )

1000 Peachtree Street N.E.
Atlanta, GA 30309-4470
United States

Washington University in St. Louis ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

Guofu Zhou (Contact Author)

Washington University in St. Louis - John M. Olin Business School ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
3,890
Abstract Views
12,664
Rank
5,586
PlumX Metrics