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A machine learning-based early warning system for systemic banking crises

Author

Listed:
  • Tongyu Wang
  • Shangmei Zhao
  • Guangxiang Zhu
  • Haitao Zheng

Abstract

Econometricians construct panel logit-based early warning systems (EWSs) as the primary predictive tool to prevent incoming systemic banking crises. Considering the actual scenario of systemic banking crises, we argue that changes in economic indicators under the crisis may impact the information extraction of EWSs based on logistic regression. According to the potential limitations of the conventional EWS and properties of the machine learning algorithm, we assume that an ‘experts voting EWS’ framework can better fit characteristics of data of systemic banking crisis. Indeed, among other machine learning classifiers tested in this paper, random forest classifier simulating experts voting process is the most efficient classifier showing relatively high generalization above 80% area under the receiver operating characteristic curve on constructing the EWS. In contrast to the conventional system, an image of evidence shows that the experts voting EWS synthesizing multivariate information may be suitable for providing systemic banking systemic crises alerts in varied contexts.

Suggested Citation

  • Tongyu Wang & Shangmei Zhao & Guangxiang Zhu & Haitao Zheng, 2021. "A machine learning-based early warning system for systemic banking crises," Applied Economics, Taylor & Francis Journals, vol. 53(26), pages 2974-2992, June.
    • Handle: RePEc:taf:applec:v:53:y:2021:i:26:p:2974-2992
    DOI: 10.1080/00036846.2020.1870657
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    Citations

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    Cited by:

    1. Pedro Guerra & Mauro Castelli, 2021. "Machine Learning Applied to Banking Supervision a Literature Review," Risks, MDPI, vol. 9(7), pages 1-24, July.
    2. 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.
    3. Shu-Ling Lin & Xiao Jin, 2023. "Does ESG Predict Systemic Banking Crises? A Computational Economics Model of Early Warning Systems with Interpretable Multi-Variable LSTM based on Mixture Attention," Mathematics, MDPI, vol. 11(2), pages 1-15, January.
    4. Carbó, José Manuel & Gorjón, Sergio, 2024. "Determinants of the price of bitcoin: An analysis with machine learning and interpretability techniques," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 123-140.
    5. Jeremy K. Nguyen & Adam Karg & Abbas Valadkhani & Heath McDonald, 2022. "Predicting individual event attendance with machine learning: a ‘step-forward’ approach," Applied Economics, Taylor & Francis Journals, vol. 54(27), pages 3138-3153, June.
    6. Moreno Badia, Marialuz & Medas, Paulo & Gupta, Pranav & Xiang, Yuan, 2022. "Debt is not free," Journal of International Money and Finance, Elsevier, vol. 127(C).
    7. Zhao, Zichao & Li, Dexuan & Dai, Wensheng, 2023. "Machine-learning-enabled intelligence computing for crisis management in small and medium-sized enterprises (SMEs)," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    8. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    9. Alonso-Alvarez, Irma & Molina, Luis, 2023. "How to foresee crises? A new synthetic index of vulnerabilities for emerging economies," Economic Modelling, Elsevier, vol. 125(C).

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