Credit Growth, the Yield Curve and Financial Crisis Prediction: Evidence from a Machine Learning Approach

We develop early warning models for financial crisis prediction using machine learning techniques on macrofinancial data for 17 countries over 1870–2016. Machine learning models mostly 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 non-linear 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 crisis, financial stability, credit growth, yield curve, Shapley values, out-of-sample prediction

JEL Classification: C40, C53, E44, F30, G01

Suggested Citation: Suggested Citation

Bluwstein, Kristina and Buckmann, Marcus and Joseph, Andreas and Kang, Miao and Kapadia, Sujit and Kapadia, Sujit and Simsek, Özgür, Credit Growth, the Yield Curve and Financial Crisis Prediction: Evidence from a Machine Learning Approach (January 10, 2020). Bank of England Working Paper No. 848, January 2020, Available at SSRN: https://ssrn.com/abstract=3520659 or http://dx.doi.org/10.2139/ssrn.3520659