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

Citations

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Cited by:

  1. Simon Lloyd & Ed Manuel & Konstantin Panchev, 2024. "Foreign Vulnerabilities, Domestic Risks: The Global Drivers of GDP-at-Risk," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 72(1), pages 335-392, March.
  2. Potjagailo, Galina & Wolters, Maik H., 2023. "Global financial cycles since 1880," Journal of International Money and Finance, Elsevier, vol. 131(C).
  3. du Plessis, Emile & Fritsche, Ulrich, 2022. "New forecasting methods for an old problem: Predicting 147 years of systemic financial crises," WiSo-HH Working Paper Series 67, University of Hamburg, Faculty of Business, Economics and Social Sciences, WISO Research Laboratory.
  4. Jeremy Fouliard & Michael Howell & Hélène Rey & Vania Stavrakeva, 2020. "Answering the Queen: Machine Learning and Financial Crises," NBER Working Papers 28302, National Bureau of Economic Research, Inc.
  5. 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.
  6. 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.
  7. 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.
  8. Marcus Buckmann & Andy Haldane & Anne-Caroline Hüser, 2021. "Comparing minds and machines: implications for financial stability," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 479-508.
  9. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
  10. Peter Breyer & Stefan Girsch & Jakob Hanzl & Mario Hübler & Sophie Steininger & Elisabeth Wittig, 2023. "An analysis of Austrian banks during the high inflation period of the 1970s," Financial Stability Report, Oesterreichische Nationalbank (Austrian Central Bank), issue 45, pages 45-59.
  11. Bitetto, Alessandro & Cerchiello, Paola & Mertzanis, Charilaos, 2023. "On the efficient synthesis of short financial time series: A Dynamic Factor Model approach," Finance Research Letters, Elsevier, vol. 53(C).
  12. Jiao, Jianling & Song, Jiangfeng & Ding, Tao, 2024. "The impact of synergistic development of renewable energy and digital economy on energy intensity: Evidence from 33 countries," Energy, Elsevier, vol. 295(C).
  13. Seulki Chung, 2023. "Inside the black box: Neural network-based real-time prediction of US recessions," Papers 2310.17571, arXiv.org, revised May 2024.
  14. 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).
  15. Simona Malovaná & Josef Bajzík & Dominika Ehrenbergerová & Jan Janků, 2023. "A prolonged period of low interest rates in Europe: Unintended consequences," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 526-572, April.
  16. Tamás Kristóf, 2021. "Sovereign Default Forecasting in the Era of the COVID-19 Crisis," JRFM, MDPI, vol. 14(10), pages 1-24, October.
  17. Luca Tiozzo Pezzoli & Elisa Tosetti, 2022. "Seismonomics: Listening to the heartbeat of the economy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 288-309, December.
  18. Jiaming Liu & Chengzhang Li & Peng Ouyang & Jiajia Liu & Chong Wu, 2023. "Interpreting the prediction results of the tree‐based gradient boosting models for financial distress prediction with an explainable machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1112-1137, August.
  19. 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.
  20. Roy, Dibyendu & Zhu, Shunmin & Wang, Ruiqi & Mondal, Pradip & Ling-Chin, Janie & Roskilly, Anthony Paul, 2024. "Techno-economic and environmental analyses of hybrid renewable energy systems for a remote location employing machine learning models," Applied Energy, Elsevier, vol. 361(C).
  21. Moreno Badia, Marialuz & Medas, Paulo & Gupta, Pranav & Xiang, Yuan, 2022. "Debt is not free," Journal of International Money and Finance, Elsevier, vol. 127(C).
  22. 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).
  23. Klieber, Karin, 2024. "Non-linear dimension reduction in factor-augmented vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).
  24. Bitetto, Alessandro & Cerchiello, Paola & Mertzanis, Charilaos, 2023. "Measuring financial soundness around the world: A machine learning approach," International Review of Financial Analysis, Elsevier, vol. 85(C).
  25. Sakiru Adebola Solarin & Muhammed Sehid Gorus & Onder Ozgur, 2024. "Modelling the economic effect of inbound birth tourism: a random forest algorithm approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4223-4240, October.
  26. Suss, Joel & Treitel, Henry, 2019. "Predicting bank distress in the UK with machine learning," Bank of England working papers 831, Bank of England.
  27. Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021. "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 681-699.
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