Exploring the Factor Zoo With A Machine-Learning Portfolio

Over the years, top journals have published hundreds of characteristics to explain stock return, but many have lost significance. What fundamentally affects the time-varying significance of characteristics that survive? We combine machine-learning (ML) and portfolio analysis to uncover patterns in significant characteristics. From out-of-sample portfolio analysis, we back out important characteristics that ML models uncover. The ML portfolio's exposure alternates between investor arbitrage constraint and firm financial constraint characteristics, the timing of which aligns with credit contraction and expansion states. We explain and show how the credit cycle affects different characteristics' ability to explain cross-sectional stock return over time