Equity Factor Timing: A Two-Stage Machine Learning Approach

Equity factor investing has gained traction due to its ability to systematically capture premia for risk or behavioral reasons. However, developing a robust factor timing investment framework remains challenging. In this article, the authors propose a two-stage machine model for dynamic factor rotation, which adapts to varying market conditions. In the first stage, the authors employ both supervised and unsupervised machine learning techniques to identify dynamic market risk regimes, which reflect the prevailing economic environment. Subsequently, the second stage utilizes additional ensemble supervised machine learning methods, incorporating the features identified in the first stage, to predict factor performance within each regime. The authors’ findings demonstrate that the proposed model delivers robust results across all regimes. Consequently, this hybrid machine learning approach offers an innovative alternative for dynamic factor investment strategies, providing investors with the tools to navigate diverse market conditions.