Estimating Stock Market Betas via Machine Learning

83 Pages Posted: 1 Oct 2021 Last revised: 22 Jun 2022

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

This paper evaluates the predictive performance of machine learning techniques in estimating time-varying market betas of U.S. stocks. Compared to established estimators, machine learning-based approaches outperform from both a statistical and an economic perspective. They provide the lowest forecast errors and lead to truly ex-post market-neutral portfolios. Among the different techniques, random forests perform the best overall. Moreover, the inherent model complexity is strongly time-varying. Historical betas, as well as turnover and size signals, are the most important predictors. Compared to linear regressions, interactions and nonlinear effects substantially enhance predictive performance.

Keywords: Beta estimation, machine learning

JEL Classification: G11, G12, C58, G17

Suggested Citation

Drobetz, Wolfgang and Hollstein, Fabian and Otto, Tizian and Prokopczuk, Marcel and Prokopczuk, Marcel, Estimating Stock Market Betas via Machine Learning (September 29, 2021). 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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