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An Early Warning System for banking crises: From regression-based analysis to machine learning techniques

Author

Listed:
  • Elizabeth Jane Casabianca

    (Prometeia Associazione per le Previsioni Econometriche, and DiSeS, Polytechnic University of Marche)

  • Michele Catalano

    (Prometeia Associazione per le Previsioni Econometriche)

  • Lorenzo Forni

    (Prometeia Associazione per le Previsioni Econometriche, and DSEA, University of Padua)

  • Elena Giarda

    (Prometeia Associazione per le Previsioni Econometriche, and Cefin, University of Modena and Reggio Emilia)

  • Simone Passeri

    (Prometeia Associazione per le Previsioni Econometriche)

Abstract

Ten years after the outbreak of the 2007-2008 crisis, renewed attention is directed to money and credit fluctuations, financial crises and policy responses. By using an integrated dataset that includes 100 countries (advanced and emerging) spanning from 1970 to 2017, we propose an Early Warning System (EWS) to predict the build-up of systemic banking crises. The paper aims at (i) identifying the macroeconomic drivers of banking crises, (ii) going beyond the use of traditional discrete choice models by applying supervised machine learning (ML) and (iii) assessing the degree of countries’ exposure to systemic risks by means of predicted probabilities. Our results show that ML algorithms can have a better predictive performance than the logit models. All models deliver increasing predicted probabilities in the last years of the sample for the advanced countries, warning against the possible build-up of pre-crisis macroeconomic imbalances.

Suggested Citation

  • Elizabeth Jane Casabianca & Michele Catalano & Lorenzo Forni & Elena Giarda & Simone Passeri, 2019. "An Early Warning System for banking crises: From regression-based analysis to machine learning techniques," "Marco Fanno" Working Papers 0235, Dipartimento di Scienze Economiche "Marco Fanno".
    • Handle: RePEc:pad:wpaper:0235
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    Citations

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    Cited by:

    1. Chris Reimann, 2024. "Predicting financial crises: an evaluation of machine learning algorithms and model explainability for early warning systems," Review of Evolutionary Political Economy, Springer, vol. 5(1), pages 51-83, June.
    2. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    3. Huynh, Tran & Uebelmesser, Silke, 2024. "Early warning models for systemic banking crises: Can political indicators improve prediction?," European Journal of Political Economy, Elsevier, vol. 81(C).
    4. Paraskevi K. Salamaliki & Ioannis A. Venetis, 2024. "Fiscal Space and Policy Response to Financial Crises: Market Access and Deficit Concerns," Open Economies Review, Springer, vol. 35(2), pages 323-361, April.
    5. Wang, Xichen, 2025. "The quantile connectedness of the international housing market," Journal of International Money and Finance, Elsevier, vol. 152(C).
    6. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023. "Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach," Journal of International Economics, Elsevier, vol. 145(C).
    7. Emile du Plessis & Ulrich Fritsche, 2025. "New forecasting methods for an old problem: Predicting 147 years of systemic financial crises," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 3-40, January.
    8. 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.
    9. Pedro Guerra & Mauro Castelli & Nadine Côrte-Real, 2022. "Approaching European Supervisory Risk Assessment with SupTech: A Proposal of an Early Warning System," Risks, MDPI, vol. 10(4), pages 1-23, March.
    10. Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.

    More about this item

    Keywords

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    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • G01 - Financial Economics - - General - - - Financial Crises
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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