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Anticipating bank distress in the Eurozone: An Extreme Gradient Boosting approach

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  • Climent, Francisco
  • Momparler, Alexandre
  • Carmona, Pedro

Abstract

The banking sector plays a special role in the economy and has critical functions which are essential for economic stability. Hence, systemic banking crises disrupt financial markets and hinder global economic growth. In this study, Extreme Gradient Boosting, a state of the art machine learning method, is applied to identify a set of key leading indicators that may help predict and prevent bank failure in the Eurozone banking sector. The cross-sectional data used in this study consists of 25 annual financial ratio series for commercial banks in the Eurozone. The sample includes Eurozone listed failed and non-failed banks for the period 2006–2016. A number of early warning systems and leading indicator models have been developed to prevent bank failure. Yet the breadth and depth of the recent financial crisis indicates that these methods must improve if they are to serve as a useful tool for regulators and managers of financial institutions. Our goal is to build a classification model to determine which variables should be monitored to anticipate bank financial distress. A set of key variables are identified to anticipate bank defaults. Identifying leading indicators of bank failure is necessary so that regulators and financial institutions' management can take preventive and corrective measures before it is too late.

Suggested Citation

  • Climent, Francisco & Momparler, Alexandre & Carmona, Pedro, 2019. "Anticipating bank distress in the Eurozone: An Extreme Gradient Boosting approach," Journal of Business Research, Elsevier, vol. 101(C), pages 885-896.
    • Handle: RePEc:eee:jbrese:v:101:y:2019:i:c:p:885-896
    DOI: 10.1016/j.jbusres.2018.11.015
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    Cited by:

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    2. Carmona, Pedro & Dwekat, Aladdin & Mardawi, Zeena, 2022. "No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure," Research in International Business and Finance, Elsevier, vol. 61(C).
    3. Guerra, Pedro & Castelli, Mauro & Côrte-Real, Nadine, 2022. "Machine learning for liquidity risk modelling: A supervisory perspective," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 175-187.
    4. Małgorzata Iwanicz-Drozdowska & Krzysztof Jackowicz & Maciej Karczmarczyk, 2021. "“The Crooked Smile of TCR†: Banks’ Solvency and Restructuring Costs in the European Banking Industry," SAGE Open, , vol. 11(3), pages 21582440211, September.
    5. 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.
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    7. Peter M. Clarkson & Jordan Ponn & Gordon D. Richardson & Frank Rudzicz & Albert Tsang & Jingjing Wang, 2020. "A Textual Analysis of US Corporate Social Responsibility Reports," Abacus, Accounting Foundation, University of Sydney, vol. 56(1), pages 3-34, March.
    8. Palvia, Ajay & Vähämaa, Emilia & Vähämaa, Sami, 2020. "Female leadership and bank risk-taking: Evidence from the effects of real estate shocks on bank lending performance and default risk," Journal of Business Research, Elsevier, vol. 117(C), pages 897-909.
    9. Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2023. "Bankruptcy prediction using machine learning and Shapley additive explanations," Post-Print hal-04223161, HAL.
    10. Sami Ben Jabeur & Nicolae Stef & Pedro Carmona, 2023. "Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 715-741, February.
    11. Delogu, Marco & Lagravinese, Raffaele & Paolini, Dimitri & Resce, Giuliano, 2024. "Predicting dropout from higher education: Evidence from Italy," Economic Modelling, Elsevier, vol. 130(C).
    12. 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.
    13. Sánchez-Franco, Manuel J. & Arenas-Márquez, Francisco J. & Alonso-Dos-Santos, Manuel, 2021. "Using structural topic modelling to predict users’ sentiment towards intelligent personal agents. An application for Amazon’s echo and Google Home," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    14. Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021. "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 681-699.
    15. de Jesus, Diego Pitta & Besarria, Cássio da Nóbrega, 2023. "Machine learning and sentiment analysis: Projecting bank insolvency risk," Research in Economics, Elsevier, vol. 77(2), pages 226-238.
    16. Tsai, Chih-Fong & Sue, Kuen-Liang & Hu, Ya-Han & Chiu, Andy, 2021. "Combining feature selection, instance selection, and ensemble classification techniques for improved financial distress prediction," Journal of Business Research, Elsevier, vol. 130(C), pages 200-209.
    17. 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.
    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. Kais Tissaoui & Taha Zaghdoudi & Abdelaziz Hakimi & Mariem Nsaibi, 2023. "Do Gas Price and Uncertainty Indices Forecast Crude Oil Prices? Fresh Evidence Through XGBoost Modeling," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 663-687, August.
    20. Jabeur, Sami Ben & Gharib, Cheima & Mefteh-Wali, Salma & Arfi, Wissal Ben, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    21. Hwang, Syjung & Kim, Jina & Park, Eunil & Kwon, Sang Jib, 2020. "Who will be your next customer: A machine learning approach to customer return visits in airline services," Journal of Business Research, Elsevier, vol. 121(C), pages 121-126.
    22. Li Yao & He Ni, 2023. "Prediction of patent grant and interpreting the key determinants: an application of interpretable machine learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 4933-4969, September.

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