Peer-Reviewed Theory Does Not Help Predict the Cross-section of Stock Returns

24 Pages Posted: 28 Dec 2022

See all articles by Andrew Y. Chen

Andrew Y. Chen

Board of Governors of the Federal Reserve System

Alejandro Lopez-Lira

University of Florida - Department of Finance, Insurance and Real Estate

Tom Zimmermann

University of Cologne

Date Written: December 20, 2022

Abstract

To examine whether theory helps predict the cross-section of returns, we combine text analysis of publications with out-of-sample tests. Based on the original texts, only 18% of predictors are attributed to risk-based theory. 58% are attributed to mispricing, and 24% have uncertain origins. Post-publication, risk-based predictability decays by 65%, compared to 50% for non-risk predictors. Out-of-sample, risk-based predictors fail to outperform data-mined accounting predictors that are matched on in-sample summary statistics. Published and data-mined returns rise before in-sample periods end and fall out-of-sample at similar rates. Overall, peer-reviewed research adds little information about future mean returns above naive back testing.

Keywords: Cross-Section of Returns,Return Predictability, Machine Learning, Big Data

Suggested Citation

Chen, Andrew Y. and Lopez-Lira, Alejandro and Zimmermann, Tom, Peer-Reviewed Theory Does Not Help Predict the Cross-section of Stock Returns (December 20, 2022). Available at SSRN: https://ssrn.com/abstract=4308069 or http://dx.doi.org/10.2139/ssrn.4308069

Andrew Y. Chen

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States
202-973-6941 (Phone)

HOME PAGE: http://sites.google.com/site/chenandrewy/

Alejandro Lopez-Lira (Contact Author)

University of Florida - Department of Finance, Insurance and Real Estate ( email )

P.O. Box 117168
Gainesville, FL 32611
United States

Tom Zimmermann

University of Cologne ( email )

Albertus-Magnus-Platz
Cologne, 50923
Germany

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