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

• 成果类型: Article
• 署名作者: Bluwstein, Kristina; Buckmann, Marcus; Joseph, Andreas; Kapadia, Sujit; Simsek, Ozgur
• 署名单位: Bank of England; European Central Bank; University of Bath
• 刊物名称: JOURNAL OF INTERNATIONAL ECONOMICS
• ISSN/ISSBN: 0022-1996
• DOI: 10.1016/j.jinteco.2023.103773
• 发表日期: 2023
• 关键词: Machine learning; Financial stability; financial crises; credit growth; yield curve; Shapley values; out-of-sample prediction

摘要

We develop early warning models for financial crisis prediction by applying machine learning techniques to 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 using 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.