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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. Emile du Plessis & Ulrich Fritsche, 2025. "New forecasting methods for an old problem: Predicting 147 years of systemic financial crises," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 3-40, January.
  3. Wang, Bo & Yan, Ruolan & Chen, Yang, 2025. "Predicting abnormal capital flow episodes with machine learning methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 103(C).
  4. Zhao, Xian & Huang, Chuangxia & Yang, Xiaoguang & Cao, Jie & Yang, Xin, 2025. "Can we better predict financial crisis? The role of Laplacian-energy-like measure," International Review of Economics & Finance, Elsevier, vol. 103(C).
  5. Owoo, Natalia & Odei-Mensah, Jones, 2025. "Hierarchical clustering-based early warning model for predicting bank failures: Insights from Ghana's financial sector reforms (2017–2019)," Research in International Business and Finance, Elsevier, vol. 77(PB).
  6. James Hurley & Sudipto Karmakar & Elena Markoska & Eryk Walczak & Danny Walker, 2021. "Impacts of the Covid-19 crisis: evidence from 2 million UK SMEs," Bank of England working papers 924, Bank of England.
  7. Yang, Jie & Niu, Yanfang & Shi, Wenlei & Zhu, Kanghuan, 2025. "Predicting ESG disclosure quality through board secretaries' characteristics: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 76(C).
  8. 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.
  9. 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.
  10. Goodell, John W. & Muckley, Cal B. & Neelakantan, Parvati & Ryan, Darragh & Yu, Pei-Shan, 2025. "AI culture ‘profiling’ and anti-money laundering: Efficacy vs ethics," International Review of Financial Analysis, Elsevier, vol. 101(C).
  11. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
  12. 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.
  13. Potjagailo, Galina & Wolters, Maik H., 2023. "Global financial cycles since 1880," Journal of International Money and Finance, Elsevier, vol. 131(C).
  14. 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).
  15. Zhang, Yang & Zheng, Huanhuan, 2025. "Spillover effects of global fund flows," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 104(C).
  16. 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.
  17. Ergenç Cansu & Aktaş Rafet, 2025. "A Supervised Machine Learning in Financial Forecasting: Identifying Effective Models for the BIST100 Index," Review of Economic Perspectives, Sciendo, vol. 25(1), pages 66-90.
  18. repec:cam:camjip:2102 is not listed on IDEAS
  19. 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).
  20. Seulki Chung, 2023. "Inside the black box: Neural network-based real-time prediction of US recessions," Papers 2310.17571, arXiv.org, revised May 2024.
  21. Zhang, Ditian & Tang, Pan & Tang, Chun & Lai, Xiaobing, 2025. "Interpretable machine learning unveils nonlinear drivers of global energy risk spillovers: A TVP-VAR approach," Economic Modelling, Elsevier, vol. 151(C).
  22. 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.
  23. 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.
  24. Wang, Xichen, 2025. "The quantile connectedness of the international housing market," Journal of International Money and Finance, Elsevier, vol. 152(C).
  25. Mohamed Elhoseny & Noura Metawa & Gabor Sztano & Ibrahim M. El-hasnony, 2025. "Deep Learning-Based Model for Financial Distress Prediction," Annals of Operations Research, Springer, vol. 345(2), pages 885-907, February.
  26. 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.
  27. Tamás Kristóf, 2021. "Sovereign Default Forecasting in the Era of the COVID-19 Crisis," JRFM, MDPI, vol. 14(10), pages 1-24, October.
  28. 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.
  29. 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.
  30. 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.
  31. 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).
  32. Moreno Badia, Marialuz & Medas, Paulo & Gupta, Pranav & Xiang, Yuan, 2022. "Debt is not free," Journal of International Money and Finance, Elsevier, vol. 127(C).
  33. 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).
  34. Klieber, Karin, 2024. "Non-linear dimension reduction in factor-augmented vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).
  35. 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).
  36. 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.
  37. Joel Suss & Henry Treitel, 2019. "Predicting bank distress in the UK with machine learning," Bank of England working papers 831, Bank of England.
  38. Augusto Cerqua & Marco Letta & Gabriele Pinto, 2024. "On the (Mis)Use of Machine Learning with Panel Data," Papers 2411.09218, arXiv.org, revised May 2025.
  39. 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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