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Do Cross-Sectional Predictors Contain Systematic Information?

Published online by Cambridge University Press:  10 May 2022

Joseph Engelberg Open the ORCID record for Joseph Engelberg [Opens in a new window] ,
R. David McLean ,
Jeffrey Pontiff  and
Matthew C. Ringgenberg Open the ORCID record for Matthew C. Ringgenberg [Opens in a new window]
Joseph Engelberg
Affiliation:
University of California, San Diego Rady School of Management [email protected]
R. David McLean
Affiliation:
Georgetown University, McDonough School of Business [email protected]
Jeffrey Pontiff*
Affiliation:
Boston College, Carroll School of Management
Matthew C. Ringgenberg
Affiliation:
University of Utah, David Eccles School of Business [email protected]
*
[email protected] (corresponding author)
Rights & Permissions [Opens in a new window]

Abstract

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Firm-level variables that predict cross-sectional stock returns, such as price-to-earnings and short interest, are often averaged and used to predict market returns. Using various samples of cross-sectional predictors and accounting for the number of predictors and their interdependence, we find only weak evidence that cross-sectional predictors make good time-series predictors, especially out-of-sample. The results suggest that cross-sectional predictors do not generally contain systematic information.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 58 , Issue 3 , May 2023 , pp. 1172 - 1201
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

The authors thank an anonymous referee, Hendrik Bessembinder (the editor), John Campbell, Mike Cooper, Amit Goyal, Robin Greenwood, Campbell Harvey, Travis Johnson, Bryan Kelly, Owen Lamont, Yan Liu, Seth Pruitt, Allan Timmermann, and Michael Wolf, and conference and seminar participants at the 2018 Society for Financial Studies Cavalcade, the 2019 American Finance Association, MIT (Accounting), TCU, UC-Berkeley, University of Kentucky, University of Michigan, UNLV, University of Utah, UC Riverside, University of Virginia, Washington University in St. Louis, University of Oxford, and Warwick Business School. All errors are our own.

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