Diverging Roads: Theory-Based vs. Machine Learning-Implied Stock Risk Premia
比较了基于理论和机器学习方法在量化股票风险溢价上的表现，发现理论方法在1个月投资期有优势，机器学习在1年投资期更优，混合策略效果良好。
Abstract We compare the performance of theory-based and machine learning (ML) methods for quantifying equity risk premia and assess hybrid strategies that combine the two very different philosophies. The theory-based approach offers advantages at a one-month investment horizon, in particular, if daily frequency risk premium estimates (RPE) are needed. At the one-year horizon, ML has an edge, especially using theory-based RPE as additional feature variables. For a hybrid strategy called Theory with ML Assistance, we employ ML to account for the approximation errors of the theory-based approach. Employing random forests or an ensemble of ML models for theory support yields promising results.