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Predicting Banking Crises with Artificial Neural Networks: The Role of Nonlinearity and Heterogeneity

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  • Kim Ristolainen

Abstract

Studies of the early warning systems (EWSs) for banking crises usually rely on linear classifiers, estimated with international datasets. I construct an EWS based on an artificial neural network (ANN) model, and I also account for regional heterogeneity in order to improve the generalization ability of EWS models. All of the banking crises in my test set are then predictable at a 24‐month horizon, using information from earlier crises. For some countries, estimation with a regional dataset significantly improves the predictions. The ANN outperforms the usual logit regression, assessed by the area under the receiver operating characteristics curve.

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  • Kim Ristolainen, 2018. "Predicting Banking Crises with Artificial Neural Networks: The Role of Nonlinearity and Heterogeneity," Scandinavian Journal of Economics, Wiley Blackwell, vol. 120(1), pages 31-62, January.
    • Handle: RePEc:bla:scandj:v:120:y:2018:i:1:p:31-62
    DOI: 10.1111/sjoe.12216
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    Cited by:

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    3. Alexandr Patalaha & Maria A. Shchepeleva, 2023. "Bank Crisis Management Policies and the New Instability," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 43-60, December.
    4. Dany-Knedlik, Geraldine & Kämpfe, Martina & Knedlik, Tobias, 2021. "The appropriateness of the macroeconomic imbalance procedure for Central and Eastern European Countries," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 48(1), pages 123-139.
    5. Söhnke M. Bartram & Jürgen Branke & Mehrshad Motahari, 2020. "Artificial intelligence in asset management," Working Papers 20202001, Cambridge Judge Business School, University of Cambridge.
    6. Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Research Discussion Papers 14/2019, Bank of Finland.
    7. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    8. Maximilian Gobel & Tanya Araújo, 2020. "Indicators of Economic Crises: A Data-Driven Clustering Approach," Working Papers REM 2020/0128, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    9. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    10. repec:zbw:bofrdp:2019_014 is not listed on IDEAS
    11. 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.

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