Predicting U.S. bank failures and stress testing with machine learning algorithms

Wendi Hu, Chujian Shao and Wenyu Zhang

Finance Research Letters, 2025, vol. 75, issue C

Abstract: This study applies multiple machine learning models to forecast the bankruptcy of U.S. financial institutions from the year 2001 to 2023 using data from the Federal Deposit Insurance Corporation. To incorporate time dynamics, this paper employs exponentially weighted moving averages, enhancing the models’ predictive accuracy. The results show that the Random Forest model achieves the highest overall accuracy, while logistic regression, XGBoost, Support Vector Machine, and neural networks offer various levels of performance. Stress testing and sensitivity analysis reveal that model accuracy is heavily reliant on key financial characteristics, and severe stress conditions can significantly reduce predictive capacity.

Keywords: Bankruptcy; Financial distress; Machine learning; Forecasting (search for similar items in EconPapers)
JEL-codes: C45 C53 G01 G21 G33 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:75:y:2025:i:c:s1544612325000674

DOI: 10.1016/j.frl.2025.106802

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