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

Author

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  • Hu, Wendi
  • Shao, Chujian
  • Zhang, Wenyu

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.

Suggested Citation

  • Hu, Wendi & Shao, Chujian & Zhang, Wenyu, 2025. "Predicting U.S. bank failures and stress testing with machine learning algorithms," Finance Research Letters, Elsevier, vol. 75(C).
    • Handle: RePEc:eee:finlet:v:75:y:2025:i:c:s1544612325000674
    DOI: 10.1016/j.frl.2025.106802
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    More about this item

    Keywords

    Bankruptcy; Financial distress; Machine learning; Forecasting;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G01 - Financial Economics - - General - - - Financial Crises
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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