Estimating stock market betas via machine learning

Drobetz, W., Hollstein, F., Otto, T. and Prokopczuk, M. (2024) Estimating stock market betas via machine learning. Journal of Financial and Quantitative Analysis. ISSN 1756-6916 doi: 10.1017/S0022109024000036

Abstract/Summary

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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/117477
Identification Number/DOI	10.1017/S0022109024000036
Refereed	Yes
Divisions	Henley Business School > Finance and Accounting
Publisher	Cambridge University Press
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