Estimating Stock Market Betas via Machine Learning

Journal of Financial and Quantitative Analysis, forthcoming

99 Pages Posted: 1 Oct 2021 Last revised: 24 Mar 2024

See all articles by Wolfgang Drobetz

Wolfgang Drobetz

University of Hamburg

Fabian Hollstein

Saarland University

Tizian Otto

University of Hamburg

Marcel Prokopczuk

Leibniz Universität Hannover - Faculty of Economics and Management; University of Reading - ICMA Centre

Date Written: September 29, 2021

Abstract

Machine learning-based 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 create better market-neutral anomaly strategies and minimum variance portfolios. Among the various techniques, random foests perform 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.

Keywords: Beta estimation, machine learning, active trading strategy

JEL Classification: G11, G12, C58, G17

Suggested Citation

Drobetz, Wolfgang and Hollstein, Fabian and Otto, Tizian and Prokopczuk, Marcel, Estimating Stock Market Betas via Machine Learning (September 29, 2021). Journal of Financial and Quantitative Analysis, forthcoming, Available at SSRN: https://ssrn.com/abstract=3933048 or http://dx.doi.org/10.2139/ssrn.3933048

Wolfgang Drobetz

University of Hamburg ( email )

Moorweidenstrasse 18
Hamburg, 20148
Germany

Fabian Hollstein

Saarland University ( email )

Campus
Saarbrucken, Saarland D-66123
Germany

Tizian Otto (Contact Author)

University of Hamburg ( email )

Moorweidenstraße 18
Hamburg, 20148
Germany

Marcel Prokopczuk

Leibniz Universität Hannover - Faculty of Economics and Management ( email )

Koenigsworther Platz 1
Hannover, 30167
Germany

University of Reading - ICMA Centre ( email )

Whiteknights Park
P.O. Box 242
Reading RG6 6BA
United Kingdom

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