  Using a comprehensive sample of 2,585 bankruptcies from 1990 to 2019, we
benchmark the performance of various machine learning models in predicting
financial distress of publicly traded U.S. firms. We find that gradient boosted
trees outperform other models in one-year-ahead forecasts. Variable permutation
tests show that excess stock returns, idiosyncratic risk, and relative size are
the more important variables for predictions. Textual features derived from
corporate filings do not improve performance materially. In a credit
competition model that accounts for the asymmetric cost of default
misclassification, the survival random forest is able to capture large dollar
profits.
