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Predicting U.S. Bank Failures with MIDAS Logit Models

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  • Audrino, Francesco
  • Kostrov, Alexander
  • Ortega, Juan-Pablo

Abstract

We propose a new approach based on a generalization of the logit model to improve prediction accuracy in U.S. bank failures. Mixed-data sampling (MIDAS) is introduced in the context of a logistic regression. We also mitigate the class-imbalance problem in data and adjust the classification accuracy evaluation. In applying the suggested model to the period from 2004 to 2016, we show that it correctly classifies significantly more bank failure cases than the classic logit model, in particular for long-term forecasting horizons. Some of the largest recent bank failures in the United States that had been previously misclassified are now correctly predicted.

Suggested Citation

  • Audrino, Francesco & Kostrov, Alexander & Ortega, Juan-Pablo, 2019. "Predicting U.S. Bank Failures with MIDAS Logit Models," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(6), pages 2575-2603, December.
    • Handle: RePEc:cup:jfinqa:v:54:y:2019:i:6:p:2575-2603_10
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    Cited by:

    1. Jiang, Cuixia & Nie, Yubing & Xu, Qifa, 2023. "A MIDAS multinomial logit model with applications for bond ratings," Global Finance Journal, Elsevier, vol. 57(C).
    2. Patel, Ajay & Sorokina, Nonna & Thornton, John H., 2022. "Liquidity and bank capital structure," Journal of Financial Stability, Elsevier, vol. 62(C).
    3. Li, Zhe & Liang, Shuguang & Pan, Xianyou & Pang, Meng, 2024. "Credit risk prediction based on loan profit: Evidence from Chinese SMEs," Research in International Business and Finance, Elsevier, vol. 67(PA).
    4. Zhou, Ying & Shen, Long & Ballester, Laura, 2023. "A two-stage credit scoring model based on random forest: Evidence from Chinese small firms," International Review of Financial Analysis, Elsevier, vol. 89(C).
    5. Lee, Kangbok & Joo, Sunghoon & Baik, Hyeoncheol & Han, Sumin & In, Joonhwan, 2020. "Unbalanced data, type II error, and nonlinearity in predicting M&A failure," Journal of Business Research, Elsevier, vol. 109(C), pages 271-287.
    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).
    7. Jiang, Cuixia & Xiong, Wei & Xu, Qifa & Liu, Yezheng, 2021. "Predicting default of listed companies in mainland China via U-MIDAS Logit model with group lasso penalty," Finance Research Letters, Elsevier, vol. 38(C).
    8. Goldmann, Leonie & Crook, Jonathan & Calabrese, Raffaella, 2024. "A new ordinal mixed-data sampling model with an application to corporate credit rating levels," European Journal of Operational Research, Elsevier, vol. 314(3), pages 1111-1126.

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