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Predicting US bank failures: A comparison of logit and data mining models

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  • Zhongbo Jing
  • Yi Fang

Abstract

Predicting bank failures is important as it enables bank regulators to take timely actions to prevent bank failures or reduce the cost of rescuing banks. This paper compares the logit model and data mining models in the prediction of bank failures in the USA between 2002 and 2010 using levels and rates of change of 16 financial ratios based on a cross†section sample. The models are estimated for the in†sample period 2002–2009, while data for the year 2010 are used for out†of†sample tests. The results suggest that the logit model predicts bank failures in†sample less precisely than data mining models, but produces fewer missed failures and false alarms out†of†sample.

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  • Zhongbo Jing & Yi Fang, 2018. "Predicting US bank failures: A comparison of logit and data mining models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(2), pages 235-256, March.
    • Handle: RePEc:wly:jforec:v:37:y:2018:i:2:p:235-256
    DOI: 10.1002/for.2487
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    Cited by:

    1. Lucia Svabova & Lucia Michalkova & Marek Durica & Elvira Nica, 2020. "Business Failure Prediction for Slovak Small and Medium-Sized Companies," Sustainability, MDPI, vol. 12(11), pages 1-14, June.
    2. 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.
    3. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    4. Tânia Costa & Júlio Lobão & Luís Pacheco, 2023. "Reassessing bank monitoring models: an empirical analysis of the value of market signals in the period 2008–2020," Journal of Banking Regulation, Palgrave Macmillan, vol. 24(2), pages 206-227, June.
    5. 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.
    6. 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).

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