2022/12/22 by Emmanuel Alanis, Sudheer Chava, Alanis, Emmanuel +3 · 1 citation
Business, Management and Accounting · Computer Science · Economics, Econometrics and Finance · #Computational Finance (q-fin.CP) #Credit Risk and Financial Regulations #FOS: Computer and information sciences #FOS: Economics and business #Financial Distress and Bankruptcy Prediction #Machine Learning (cs.LG) #cs.LG #q-fin.CP
paper · pdf · doi:10.48550/arxiv.2212.12051
arxiv created 2022/12/22 · openalex publication_date 2022/12/22 · arxiv updated 2022/12/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.