Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach
Marcus Buckmann (),
Miao Kang (),
Sujit Kapadia and
Özgür Simsek ()
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Marcus Buckmann: Bank of England, Postal: Bank of England, Threadneedle Street, London, EC2R 8AH
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
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
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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Persistent link: https://EconPapers.repec.org/RePEc:boe:boeewp:0848
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