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Does machine learning help us predict banking crises?

Citations

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Cited by:

  1. Chris Reimann, 2024. "Predicting financial crises: an evaluation of machine learning algorithms and model explainability for early warning systems," Review of Evolutionary Political Economy, Springer, vol. 5(1), pages 51-83, June.
  2. Grodecka-Messi, Anna & Kenny, Seán & Ögren, Anders, 2021. "Predictors of bank distress: The 1907 crisis in Sweden," Explorations in Economic History, Elsevier, vol. 80(C).
  3. Beutel, Johannes & Metiu, Norbert & Stockerl, Valentin, 2021. "Toothless tiger with claws? Financial stability communication, expectations, and risk-taking," Journal of Monetary Economics, Elsevier, vol. 120(C), pages 53-69.
  4. Casabianca, Elizabeth Jane & Catalano, Michele & Forni, Lorenzo & Giarda, Elena & Passeri, Simone, 2022. "A machine learning approach to rank the determinants of banking crises over time and across countries," Journal of International Money and Finance, Elsevier, vol. 129(C).
  5. Hartwig, Benny & Meinerding, Christoph & Schüler, Yves S., 2021. "Identifying indicators of systemic risk," Journal of International Economics, Elsevier, vol. 132(C).
  6. João Gabriel Moraes Souza & Daniel Tavares Castro & Yaohao Peng & Ivan Ricardo Gartner, 2024. "A Machine Learning-Based Analysis on the Causality of Financial Stress in Banking Institutions," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1857-1890, September.
  7. Pham, Xuan T.T. & Ho, Tin H., 2021. "Using boosting algorithms to predict bank failure: An untold story," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 40-54.
  8. Ponomarenko, Alexey & Tatarintsev, Stas, 2023. "Incorporating financial development indicators into early warning systems," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
  9. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
  10. Huynh, Tran & Uebelmesser, Silke, 2024. "Early warning models for systemic banking crises: Can political indicators improve prediction?," European Journal of Political Economy, Elsevier, vol. 81(C).
  11. Ellis, Scott & Sharma, Satish & Brzeszczyński, Janusz, 2022. "Systemic risk measures and regulatory challenges," Journal of Financial Stability, Elsevier, vol. 61(C).
  12. Wang, Peiwan & Zong, Lu, 2023. "Does machine learning help private sectors to alarm crises? Evidence from China’s currency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
  13. Nakatani, Ryota, 2020. "Macroprudential policy and the probability of a banking crisis," Journal of Policy Modeling, Elsevier, vol. 42(6), pages 1169-1186.
  14. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
  15. Bui, Dien Giau & Chen, Yan-Shing & Hsu, Hsing-Hua & Lin, Chih-Yung, 2020. "Labor unions and bank risk culture: evidence from the financial crisis," Journal of Financial Stability, Elsevier, vol. 51(C).
  16. Susanna Levantesi & Gabriella Piscopo, 2020. "The Importance of Economic Variables on London Real Estate Market: A Random Forest Approach," Risks, MDPI, vol. 8(4), pages 1-17, October.
  17. 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).
  18. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
  19. Metiu, Norbert, 2022. "A composite indicator of financial conditions for Germany," Technical Papers 03/2022, Deutsche Bundesbank.
  20. 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.
  21. Sreenivasulu Puli & Nagaraju Thota & A. C. V. Subrahmanyam, 2024. "Assessing Machine Learning Techniques for Predicting Banking Crises in India," JRFM, MDPI, vol. 17(4), pages 1-16, March.
  22. Solomon Y. Deku & Alper Kara & Artur Semeyutin, 2021. "The predictive strength of MBS yield spreads during asset bubbles," Review of Quantitative Finance and Accounting, Springer, vol. 56(1), pages 111-142, January.
  23. Hristov, Nikolay & Roth, Markus, 2022. "Uncertainty shocks and systemic-risk indicators," Journal of International Money and Finance, Elsevier, vol. 122(C).
  24. Mauro Paoloni & Massimiliano Celli, 2023. "The Covid-19 Pandemic and the Eurozone: A Reconnaissance of E.U. Financial Assistance to Counteract the Coronavirus’s Perfect Storm," International Journal of Business and Management, Canadian Center of Science and Education, vol. 16(7), pages 1-72, February.
  25. Leonard Sabetti & Ronald Heijmans, 2020. "Shallow or deep? Detecting anomalous flows in the Canadian Automated Clearing and Settlement System using an autoencoder," Working Papers 681, DNB.
  26. Chau, Michael & Lin, Chih-Yung & Lin, Tse-Chun, 2020. "Wisdom of crowds before the 2007–2009 global financial crisis," Journal of Financial Stability, Elsevier, vol. 48(C).
  27. 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).
  28. 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).
  29. Li Xian Liu & Shuangzhe Liu & Milind Sathye, 2021. "Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research," JRFM, MDPI, vol. 14(10), pages 1-24, October.
  30. Zongxin Zhang & Ying Chen, 2022. "Tail Risk Early Warning System for Capital Markets Based on Machine Learning Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 901-923, October.
  31. Kristóf, Tamás & Virág, Miklós, 2022. "EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks," Research in International Business and Finance, Elsevier, vol. 61(C).
  32. 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.
  33. Magnus Saß, 2024. "Detecting excessive credit growth: An approach based on structural counterfactuals," Berlin School of Economics Discussion Papers 0046, Berlin School of Economics.
  34. du Plessis, Emile, 2022. "Multinomial modeling methods: Predicting four decades of international banking crises," Economic Systems, Elsevier, vol. 46(2).
  35. Dichtl, Hubert & Drobetz, Wolfgang & Otto, Tizian, 2023. "Forecasting Stock Market Crashes via Machine Learning," Journal of Financial Stability, Elsevier, vol. 65(C).
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