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

Journal of International Economics, 2023, vol. 145, issue C

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.

Keywords: Machine learning; Financial crises; Financial stability; Credit growth; Yield curve; Shapley values; Out-of-sample prediction (search for similar items in EconPapers)
JEL-codes: C40 C53 E44 F30 G01 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (5)

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Related works:
Working Paper: Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach (2021)
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:eee:inecon:v:145:y:2023:i:c:s0022199623000594

DOI: 10.1016/j.jinteco.2023.103773

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