Machine Learning Approaches to Factor Timing in Equity Markets

We investigate the use of gradient boosting and neural network models for timing exposure to traditional Fama-French factors. Our analysis suggests that while factors exhibit time-varying premia, out-of-sample predictability remains limited. We propose a regime-switching framework that improves risk-adjusted returns by 15% over static factor exposure.