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Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios

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  • Le, Hong Hanh
  • Viviani, Jean-Laurent

Abstract

This research compares the accuracy of two approaches: traditional statistical techniques and machine learning techniques, which attempt to predict the failure of banks. A sample of 3000 US banks (1438 failures and 1562 active banks) is investigated by two traditional statistical approaches (Discriminant analysis and Logistic regression) and three machine learning approaches (Artificial neural network, Support Vector Machines and k-nearest neighbors). For each bank, data were collected for a 5-year period before they become inactive. 31 financial ratios extracted from bank financial reports covered 5 main aspects: Loan quality, Capital quality, Operations efficiency, Profitability and Liquidity. The empirical result reveals that the artificial neural network and k-nearest neighbor methods are the most accurate.

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  • Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    • Handle: RePEc:eee:riibaf:v:44:y:2018:i:c:p:16-25
    DOI: 10.1016/j.ribaf.2017.07.104
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    17. 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.
    18. 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).
    19. Yuan, Kunpeng & Chi, Guotai & Zhou, Ying & Yin, Hailei, 2022. "A novel two-stage hybrid default prediction model with k-means clustering and support vector domain description," Research in International Business and Finance, Elsevier, vol. 59(C).
    20. Suss, Joel & Treitel, Henry, 2019. "Predicting bank distress in the UK with machine learning," Bank of England working papers 831, Bank of England.
    21. Buckmann, Marcus & Gallego Marquez, Paula & Gimpelewicz, Mariana & Kapadia, Sujit & Rismanchi, Katie, 2021. "The more the merrier? Evidence from the global financial crisis on the value of multiple requirements in bank regulation," Bank of England working papers 905, Bank of England.

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    More about this item

    Keywords

    Failure prediction; Intelligent techniques; Artificial neural network; Support vector machines; K-nearest neighbors; US banks;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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