Does Peer-Reviewed Research Help Predict Stock Returns?

55 Pages Posted: 28 Dec 2022 Last revised: 5 Feb 2024

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: October 27, 2023

Abstract

Mining 29,000 accounting ratios for t-statistics over 2.0 leads to cross-sectional return predictability similar to the peer review process. For both methods, about 50% of predictability remains after the original sample periods. Predictors supported by peer-reviewed risk explanations or equilibrium models underperform other predictors post-sample, suggesting peer review systematically mislabels mispricing as risk, though only 20% of predictors are labelled as risk. Data mining generates other features of peer review including the rise in returns as original sample periods end and the speed of post-sample decay. It also uncovers themes like investment, issuance, and accruals-decades before they are published.

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

Suggested Citation

Chen, Andrew Y. and Lopez-Lira, Alejandro and Zimmermann, Tom, Does Peer-Reviewed Research Help Predict Stock Returns? (October 27, 2023). Jacobs Levy Equity Management Center for Quantitative Financial Research Paper, 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

HOME PAGE: http://alejandrolopezlira.site/

Tom Zimmermann

University of Cologne ( email )

Albertus-Magnus-Platz
Cologne, 50923
Germany

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