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

Kristina Bluwstein, Marcus Buckmann, Andreas Joseph, Miao Kang (), Sujit Kapadia and Özgür Simsek ()
Additional contact information
Miao Kang: Bank of England, Postal: Bank of England, Threadneedle Street, London, EC2R 8AH
Özgür Simsek: University of Bath

No 848, Bank of England working papers from Bank of England

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: C40 C53 E44 F30 G01 (search for similar items in EconPapers)
Pages: 65 pages
Date: 2020-01-03
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fdg, nep-gth, nep-mac, nep-mon and nep-rmg
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Citations: View citations in EconPapers (25)

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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 (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:boe:boeewp:0848

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