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Using boosting algorithms to predict bank failure: An untold story

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  • Pham, Xuan T.T.
  • Ho, Tin H.

Abstract

From a modeling point of view, our work provides a novel approach to better use XGBoost for bank failure prediction, determining the essential technical aspects that can improve the predictive accuracy. Of these technical aspects, the two crucial factors are assigning correct values to target variables and careful predictor selection (through ANOVA, correlation, information value tests, and weight of evidence). We also highlight that bank failure could be predicted four to five quarters earlier when all predictive signals simultaneously appear. Hence, we strongly suggest using quarterly data instead of yearly data. In addition to practical implications, our present work also contributed to the existing literature. We confirm the results of existing studies that emphasized that XGBoost has strong predictive power (Carmona, Climent, and Momparler (2018)). Moreover, we provide evidence that XGBoost outperforms other models in the same boosting family, including gradient boosting and AdaBoost, through an intensive comparison of predictive power. These contributions might facilitate future work on bank failure prediction.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:reveco:v:76:y:2021:i:c:p:40-54
    DOI: 10.1016/j.iref.2021.05.005
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    3. Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).
    4. 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).
    5. 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.

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