IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v75y2025ics1544612325000674.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612325000674
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2025.106802?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Manel Hamdi & Sami Mestiri & Adnène Arbi, 2024. "Artificial Intelligence Techniques for Bankruptcy Prediction of Tunisian Companies: An Application of Machine Learning and Deep Learning-Based Models," JRFM, MDPI, vol. 17(4), pages 1-14, March.
    2. Gary Richardson & Marco Del Angel & Michael Gou, 2024. "Bank Failures and Economic Activity: Evidence from the Progressive Era," NBER Working Papers 32345, National Bureau of Economic Research, Inc.
    3. Jiaming Liu & Chengzhang Li & Peng Ouyang & Jiajia Liu & Chong Wu, 2023. "Interpreting the prediction results of the tree‐based gradient boosting models for financial distress prediction with an explainable machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1112-1137, August.
    4. Antunes, António & Bonfim, Diana & Monteiro, Nuno & Rodrigues, Paulo M.M., 2018. "Forecasting banking crises with dynamic panel probit models," International Journal of Forecasting, Elsevier, vol. 34(2), pages 249-275.
    5. Raymond A.K. Cox & Grace W.-Y. Wang, 2014. "Predicting the US bank failure: A discriminant analysis," Economic Analysis and Policy, Elsevier, vol. 44(2), pages 202-211.
    6. Kolari, James & Glennon, Dennis & Shin, Hwan & Caputo, Michele, 2002. "Predicting large US commercial bank failures," Journal of Economics and Business, Elsevier, vol. 54(4), pages 361-387.
    7. Pham, Xuan T.T. & Ho, Tin H., 2021. "Using boosting algorithms to predict bank failure: An untold story," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 40-54.
    8. del Angel, Marco & Richardson, Gary & Gou, Michael, 2024. "Bank failures and economic activity: Evidence from the Progressive Era," Explorations in Economic History, Elsevier, vol. 94(C).
    9. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    10. Cleary, Sean & Hebb, Greg, 2016. "An efficient and functional model for predicting bank distress: In and out of sample evidence," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 101-111.
    11. Gogas, Periklis & Papadimitriou, Theophilos & Agrapetidou, Anna, 2018. "Forecasting bank failures and stress testing: A machine learning approach," International Journal of Forecasting, Elsevier, vol. 34(3), pages 440-455.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    2. Li Xian Liu & Shuangzhe Liu & Milind Sathye, 2021. "Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    3. Kristóf, Tamás & Virág, Miklós, 2022. "EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks," Research in International Business and Finance, Elsevier, vol. 61(C).
    4. Theophilos Papadimitriou & Periklis Gogas & Anna Agrapetidou, 2022. "The resilience of the U.S. banking system," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 2819-2835, July.
    5. Qiongbing Wu & Rebel A. Cole, 2024. "Macroeconomic conditions and bank failure," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1212-1234, August.
    6. Petropoulos, Anastasios & Siakoulis, Vasilis & Stavroulakis, Evangelos & Vlachogiannakis, Nikolaos E., 2020. "Predicting bank insolvencies using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1092-1113.
    7. Moffo, Ahmadou Mustapha Fonton, 2024. "A machine learning approach in stress testing US bank holding companies," International Review of Financial Analysis, Elsevier, vol. 95(PC).
    8. Gogas, Periklis & Papadimitriou, Theophilos & Agrapetidou, Anna, 2018. "Forecasting bank failures and stress testing: A machine learning approach," International Journal of Forecasting, Elsevier, vol. 34(3), pages 440-455.
    9. repec:mth:ijafr8:v:8:y:2018:i:3:p:39-50 is not listed on IDEAS
    10. Cullen F. Goenner, 2020. "Uncertain times and early predictions of bank failure," The Financial Review, Eastern Finance Association, vol. 55(4), pages 583-601, November.
    11. Aykut Ekinci & Safa Sen, 2024. "Forecasting Bank Failure in the U.S.: A Cost-Sensitive Approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3161-3179, December.
    12. Jaremski, Matthew & Richardson, Gary & Vossmeyer, Angela, 2025. "Signals and stigmas from banking interventions: Lessons from the Bank Holiday of 1933," Journal of Financial Economics, Elsevier, vol. 163(C).
    13. Papanikolaou, Nikolaos I., 2018. "To be bailed out or to be left to fail? A dynamic competing risks hazard analysis," Journal of Financial Stability, Elsevier, vol. 34(C), pages 61-85.
    14. Joel Suss & Henry Treitel, 2019. "Predicting bank distress in the UK with machine learning," Bank of England working papers 831, Bank of England.
    15. Manthoulis, Georgios & Doumpos, Michalis & Zopounidis, Constantin & Galariotis, Emilios, 2020. "An ordinal classification framework for bank failure prediction: Methodology and empirical evidence for US banks," European Journal of Operational Research, Elsevier, vol. 282(2), pages 786-801.
    16. Zhiyong Li & Chen Feng & Ying Tang, 2022. "Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis," Annals of Operations Research, Springer, vol. 315(1), pages 279-315, August.
    17. Iwanicz-Drozdowska, Małgorzata & Witkowski, Bartosz, 2022. "Regulation and supervision of the European banking industry. Does one size fit all?," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 113-129.
    18. Jean Armand Gnagne & Kevin Moran, 2018. "Monitoring Bank Failures in a Data-Rich Environment," Cahiers de recherche 1815, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    19. Elena G. Shershneva, 2024. "CAMELS parameters’ impact on the risk of losing financial stability: The case of Russian banks," Journal of New Economy, Ural State University of Economics, vol. 25(2), pages 130-152, July.
    20. Jorge E. Galán, 2021. "CREWS: a CAMELS-based early warning system of systemic risk in the banking sector," Occasional Papers 2132, Banco de España.
    21. Jean-Armand Gnagne & Kevin Moran, 2020. "Forecasting Bank Failures in a Data-Rich Environment," Working Papers 20-13, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:75:y:2025:i:c:s1544612325000674. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.