  We use supervised learning to identify factors that predict the cross-section
of returns and maximum drawdown for stocks in the US equity market. Our data
run from January 1970 to December 2019 and our analysis includes ordinary least
squares, penalized linear regressions, tree-based models, and neural networks.
We find that the most important predictors tended to be consistent across
models, and that non-linear models had better predictive power than linear
models. Predictive power was higher in calm periods than in stressed periods.
Environmental, social, and governance indicators marginally impacted the
predictive power of non-linear models in our data, despite their negative
correlation with maximum drawdown and positive correlation with returns. Upon
exploring whether ESG variables are captured by some models, we find that ESG
data contribute to the prediction nonetheless.
