Estimating Stock Market Betas via Machine Learning
83 Pages Posted: 1 Oct 2021 Last revised: 22 Jun 2022
Date Written: September 29, 2021
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
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