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Estimating Stock Market Betas via Machine Learning

Published online by Cambridge University Press:  08 February 2024

Wolfgang Drobetz ,
Fabian Hollstein Open the ORCID record for Fabian Hollstein [Opens in a new window] ,
Tizian Otto  and
Marcel Prokopczuk
Wolfgang Drobetz*
Affiliation:
University of Hamburg, Faculty of Business Administration
Fabian Hollstein
Affiliation:
Saarland University, School of Human and Business Sciences [email protected]
Tizian Otto
Affiliation:
University of Hamburg, Faculty of Business Administration [email protected]
Marcel Prokopczuk
Affiliation:
Leibniz University, Hannover School of Economics and Management [email protected]
*
[email protected] (corresponding author)
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Abstract

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Machine learning-based stock market beta estimators outperform established benchmark models both statistically and economically. Analyzing the predictability of time-varying market betas of U.S. stocks, we document that machine learning-based estimators produce the lowest forecast and hedging errors. They also help to create better market-neutral anomaly strategies and minimum variance portfolios. Among the various techniques, random forests perform the best overall. Model complexity is highly time-varying. Historical stock market betas, turnover, and size are the most important predictors. Compared to linear regressions, allowing for nonlinearity and interactions significantly improves predictive performance.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , First View , pp. 1 - 37
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), 2024. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

We thank two anonymous referees, Yakov Amihud, Michael Bauer, Wolfgang Bessler, Axel Cabrol, Mikhail Chernov, Hubert Dichtl, Thierry Foucault (the editor), Kay Giesecke, Lisa Goldberg, Valentin Haddad, Barney Hartman-Glaser, Bernard Herskovic, Bryan Kelly, Serhiy Kozak, Markus Leippold, Martin Lettau, Lars A. Lochstoer, Harald Lohre, Tyler Muir, Andreas Neuhierl, Terrance Odean, Stavros Panageas, Markus Pelger, Tatjana Puhan, Carsten Rother, Boris Vallee, Michael Weber, and Ivo Welch for helpful comments and suggestions.

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