Estimating stock market betas via machine learning

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 show that machine learning-based estimators produce the lowest forecasting and hedging errors. They also help create better market-neutral anomaly strategies and minimum variance portfolios. Among the various techniques, random forests perform best overall. Model complexity is highly time-varying. Historical betas, turnover, and size are the most important predictors. Compared to linear regressions, allowing for nonlinearity and interactions significantly improves the predictive performance.