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Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach

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  • Bluwstein, Kristina
  • Buckmann, Marcus
  • Joseph, Andreas
  • Kapadia, Sujit
  • Şimşek, Özgür

Abstract

We develop early warning models for financial crisis prediction applying machine learning techniques on macrofinancial data for 17 countries over 1870–2016. Most nonlinear machine learning models outperform logistic regression in out-of-sample predictions and forecasting. We identify economic drivers of our machine learning models by applying a novel framework based on Shapley values, uncovering nonlinear relationships between the predictors and crisis risk. Throughout, the most important predictors are credit growth and the slope of the yield curve, both domestically and globally. A flat or inverted yield curve is of most concern when nominal interest rates are low and credit growth is high.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:inecon:v:145:y:2023:i:c:s0022199623000594
    DOI: 10.1016/j.jinteco.2023.103773
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    More about this item

    Keywords

    Machine learning; Financial crises; Financial stability; Credit growth; Yield curve; Shapley values; Out-of-sample prediction;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • F30 - International Economics - - International Finance - - - General
    • G01 - Financial Economics - - General - - - Financial Crises

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