Estimating Stock Market Betas via Machine Learning
研究发现基于机器学习的股票市场贝塔系数估计器在统计和经济上均优于传统基准模型，其中随机森林表现最佳，且非线性与交互作用显著提升预测性能。
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.