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

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

No 2614, Working Paper Series from European Central Bank

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

Keywords: credit growth; machine learning; Shapley values; yield curve; financial crises; financial stability (search for similar items in EconPapers)
Date: 2021-11
New Economics Papers: this item is included in nep-big, nep-cmp, nep-mac and nep-mon
Note: 2453540
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Citations: View citations in EconPapers (7)

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Related works:
Journal Article: Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach (2023)
Working Paper: Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20212614

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