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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 by applying machine learning techniques to macrofinancial data for 17 countries over 1870–2016. Most nonlin-ear machine learning models outperform logistic regression in out-of-sample predictions and forecasting. We identify economic drivers of our machine learning models using a novel framework based on Shapley values, uncovering nonlinear relationships between the predic-tors 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. JEL Classification: C40, C53, E44, F30, G01

Suggested Citation

  • Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2021. "Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach," Working Paper Series 2614, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20212614
    Note: 2453540
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    More about this item

    Keywords

    credit growth; machine learning; Shapley values; yield curve; financial crises; financial stability;
    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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