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Estimating Stock Market Betas via Machine Learning

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  • Drobetz, Wolfgang
  • Hollstein, Fabian
  • Otto, Tizian
  • Prokopczuk, Marcel

Abstract

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.

Suggested Citation

  • Drobetz, Wolfgang & Hollstein, Fabian & Otto, Tizian & Prokopczuk, Marcel, 2025. "Estimating Stock Market Betas via Machine Learning," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 60(3), pages 1074-1110, May.
    • Handle: RePEc:cup:jfinqa:v:60:y:2025:i:3:p:1074-1110_1
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