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Spotting the Danger Zone: Forecasting Financial Crises With Classification Tree Ensembles and Many Predictors

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  • Felix Ward

Abstract

This paper introduces classification tree ensembles (CTEs) to the banking crisis forecasting literature. I show that CTEs substantially improve out‐of‐sample forecasting performance over best‐practice early‐warning systems. CTEs enable policymakers to correctly forecast 80% of crises with a 20% probability of incorrectly forecasting a crisis. These findings are based on a long‐run sample (1870–2011), and two broad post‐1970 samples which together cover almost all known systemic banking crises. I show that the marked improvement in forecasting performance results from the combination of many classification trees into an ensemble, and the use of many predictors. Copyright © 2016 John Wiley & Sons, Ltd.

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  • Felix Ward, 2017. "Spotting the Danger Zone: Forecasting Financial Crises With Classification Tree Ensembles and Many Predictors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 359-378, March.
    • Handle: RePEc:wly:japmet:v:32:y:2017:i:2:p:359-378
    DOI: 10.1002/jae.2525
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    Cited by:

    1. Moritz Schularick & Lucas ter Steege & Felix Ward, 2021. "Leaning against the Wind and Crisis Risk," American Economic Review: Insights, American Economic Association, vol. 3(2), pages 199-214, June.
    2. Nina Boyarchenko & Giovanni Favara & Moritz Schularick, 2022. "Financial Stability Considerations for Monetary Policy: Empirical Evidence and Challenges," Staff Reports 1003, Federal Reserve Bank of New York.
    3. du Plessis, Emile & Fritsche, Ulrich, 2022. "New forecasting methods for an old problem: Predicting 147 years of systemic financial crises," WiSo-HH Working Paper Series 67, University of Hamburg, Faculty of Business, Economics and Social Sciences, WISO Research Laboratory.
    4. Fernandez-Gallardo, Alvaro, 2023. "Preventing financial disasters: Macroprudential policy and financial crises," European Economic Review, Elsevier, vol. 151(C).
    5. 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).
    6. 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.
    7. 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.
    8. Ristolainen, Kim & Roukka, Tomi & Nyberg, Henri, 2024. "A thousand words tell more than just numbers: Financial crises and historical headlines," Journal of Financial Stability, Elsevier, vol. 70(C).
    9. 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.
    10. Piotr Pomorski & Denise Gorse, 2023. "Improving Portfolio Performance Using a Novel Method for Predicting Financial Regimes," Papers 2310.04536, arXiv.org.
    11. Bitetto, Alessandro & Cerchiello, Paola & Mertzanis, Charilaos, 2023. "Measuring financial soundness around the world: A machine learning approach," International Review of Financial Analysis, Elsevier, vol. 85(C).

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