Using boosting algorithms to predict bank failure: An untold story
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DOI: 10.1016/j.iref.2021.05.005
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Cited by:
- Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
- Hamed Mirashk & Amir Albadvi & Mehrdad Kargari & Mohammad Ali Rastegar, 2024. "News Sentiment and Liquidity Risk Forecasting: Insights from Iranian Banks," Risks, MDPI, vol. 12(11), pages 1-32, October.
- 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.
- Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).
- 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).
- 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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Keywords
U.S. banks; Bank failure prediction; Boosting algorithms; XGBoost; Variable selection techniques; Target variables;All these keywords.
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