Cross-Sectional Expected Returns: New Fama-MacBeth Regressions in the Era of Machine Learning

57 Pages Posted: 13 Jun 2018 Last revised: 30 Apr 2023

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: April 28, 2023

Abstract

We extend the Fama-MacBeth regression framework for cross-sectional return prediction to incorporate big data and machine learning. Extensions for improving cross-sectional return prediction include penalized regression, forecast ensembles, and random features to accommodate nonlinearities. We also develop tools for assessing cross-sectional return forecasts using the Fama-MacBeth approach, including a generalization of the popular out-of-sample R-squared statistic. Applying our new methods to predict cross-sectional stock returns using over 200 firm characteristics, we find that the Fama-MacBeth regression framework augmented by machine learning significantly improves cross-sectional return forecasts.

Keywords: Penalized regression, Forecast combination, Forecast Encompassing, Random features, Characteristic payoffs, 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 (April 28, 2023). 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/

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