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

Author

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  • Bluwstein, Kristina

    (Bank of England)

  • Buckmann, Marcus

    (Bank of England)

  • Joseph, Andreas

    (Bank of England and King’s College London)

  • Kang, Miao

    (Bank of England)

  • Kapadia, Sujit

    (European Central Bank)

  • Simsek, Özgür

    (University of Bath)

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.

Suggested Citation

  • Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kang, Miao & Kapadia, Sujit & Simsek, Özgür, 2020. "Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach," Bank of England working papers 848, Bank of England.
  • Handle: RePEc:boe:boeewp:0848
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    3. Jeremy Fouliard & Michael Howell & Hélène Rey, 2020. "Answering the Queen: Machine Learning and Financial Crises," NBER Working Papers 28302, National Bureau of Economic Research, Inc.
    4. Hurley, James & Karmakar, Sudipto & Markoska, Elena & Walczak, Eryk & Walker, Danny, 2021. "Impacts of the Covid-19 crisis: evidence from 2 million UK SMEs," Bank of England working papers 924, Bank of England.
    5. Barbara Jarmulska, 2022. "Random forest versus logit models: Which offers better early warning of fiscal stress?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 455-490, April.
    6. Hyeongwoo Kim & Wen Shi, 2021. "Forecasting financial vulnerability in the USA: A factor model approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 439-457, April.
    7. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    8. Buckmann, Marcus & Haldane, Andy & Hüser, Anne-Caroline, 2021. "Comparing minds and machines: implications for financial stability," Bank of England working papers 937, Bank of England.
    9. Ademmer, Martin & Beckmann, Joscha & Bode, Eckhardt & Boysen-Hogrefe, Jens & Funke, Manuel & Hauber, Philipp & Heidland, Tobias & Hinz, Julian & Jannsen, Nils & Kooths, Stefan & Söder, Mareike & Stame, 2021. "Big Data in der makroökonomischen Analyse," Kieler Beiträge zur Wirtschaftspolitik 32, Kiel Institute for the World Economy (IfW Kiel).
    10. Tamás Kristóf, 2021. "Sovereign Default Forecasting in the Era of the COVID-19 Crisis," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    11. Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.
    12. Moreno Badia, Marialuz & Medas, Paulo & Gupta, Pranav & Xiang, Yuan, 2022. "Debt is not free," Journal of International Money and Finance, Elsevier, vol. 127(C).
    13. Truong, Chi & Sheen, Jeffrey & Trück, Stefan & Villafuerte, James, 2022. "Early warning systems using dynamic factor models: An application to Asian economies," Journal of Financial Stability, Elsevier, vol. 58(C).
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    More about this item

    Keywords

    Machine learning; financial crisis; financial stability; credit growth; yield curve; Shapley values; out-of-sample prediction;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • F30 - International Economics - - International Finance - - - General
    • G01 - Financial Economics - - General - - - Financial Crises

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