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Predicting bank insolvencies using machine learning techniques

Citations

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Cited by:

  1. Wosnitza, Jan Henrik, 2022. "Calibration alternatives to logistic regression and their potential for transferring the dispersion of discriminatory power into uncertainties of probabilities of default," Discussion Papers 04/2022, Deutsche Bundesbank.
  2. Citterio, Alberto & King, Timothy, 2023. "The role of Environmental, Social, and Governance (ESG) in predicting bank financial distress," Finance Research Letters, Elsevier, vol. 51(C).
  3. Evžen Kočenda & Ichiro Iwasaki, 2022. "Bank survival around the world: A meta‐analytic review," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 108-156, February.
  4. Katsafados, Apostolos G. & Leledakis, George N. & Panagiotou, Nikolaos P. & Pyrgiotakis, Emmanouil G., 2024. "Can central bankers’ talk predict bank stock returns? A machine learning approach," MPRA Paper 122899, University Library of Munich, Germany.
  5. Ruize Gao & Shaoze Cui & Yu Wang & Wei Xu, 2025. "Predicting financial distress in high-dimensional imbalanced datasets: a multi-heterogeneous self-paced ensemble learning framework," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-34, December.
  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. Wei Miao & Jad Beyhum & Jonas Striaukas & Ingrid Van Keilegom, 2025. "High-dimensional censored MIDAS logistic regression for corporate survival forecasting," Papers 2502.09740, arXiv.org.
  8. Xi, Haomeng & Wang, Jizhou, 2024. "Social governance, family happiness, and financial inclusion," Finance Research Letters, Elsevier, vol. 61(C).
  9. Imad Bou-Hamad & Abdel Latef Anouze & Ibrahim H. Osman, 2022. "A cognitive analytics management framework to select input and output variables for data envelopment analysis modeling of performance efficiency of banks using random forest and entropy of information," Annals of Operations Research, Springer, vol. 308(1), pages 63-92, January.
  10. Xinlin Wang & Zs'ofia Kraussl & Mats Brorsson, 2024. "Datasets for Advanced Bankruptcy Prediction: A survey and Taxonomy," Papers 2411.01928, arXiv.org.
  11. Petr Jakubik & Bogdan Gabriel Moinescu, 2023. "What is the optimal capital ratio implying a stable European banking system?," International Finance, Wiley Blackwell, vol. 26(3), pages 324-343, December.
  12. Jiang, Cuiqing & Lyu, Ximei & Yuan, Yufei & Wang, Zhao & Ding, Yong, 2022. "Mining semantic features in current reports for financial distress prediction: Empirical evidence from unlisted public firms in China," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1086-1099.
  13. 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.
  14. Blanco-Oliver Antonio & Lara-Rubio Juan & Irimia-Diéguez Ana & Liébana-Cabanillas Francisco, 2024. "Examining user behavior with machine learning for effective mobile peer-to-peer payment adoption," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-30, December.
  15. Veganzones, David & Séverin, Eric & Chlibi, Souhir, 2023. "Influence of earnings management on forecasting corporate failure," International Journal of Forecasting, Elsevier, vol. 39(1), pages 123-143.
  16. Aleksandra Szymura, 2022. "Risk Assessment of Polish Joint Stock Companies: Prediction of Penalties or Compensation Payments," Risks, MDPI, vol. 10(5), pages 1-22, May.
  17. Abdel Latef Anouze & Imad Bou-Hamad, 2021. "Inefficiency source tracking: evidence from data envelopment analysis and random forests," Annals of Operations Research, Springer, vol. 306(1), pages 273-293, November.
  18. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
  19. Yavuz GÜL & Serpil ALTINIRMAK, 2025. "Predicting Financial Failure: Empirical Evidence from Publicly – Quoted Firms in Developed and Developing Countries," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 10(1), pages 107-126.
  20. Durand, Pierre & Le Quang, Gaëtan, 2022. "Banks to basics! Why banking regulation should focus on equity," European Journal of Operational Research, Elsevier, vol. 301(1), pages 349-372.
  21. Borchert, Philipp & Coussement, Kristof & De Caigny, Arno & De Weerdt, Jochen, 2023. "Extending business failure prediction models with textual website content using deep learning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 348-357.
  22. Hossein Hassani & Xu Huang & Emmanuel Silva & Mansi Ghodsi, 2020. "Deep Learning and Implementations in Banking," Annals of Data Science, Springer, vol. 7(3), pages 433-446, September.
  23. Jakub Horak & Tomas Krulicky & Zuzana Rowland & Veronika Machova, 2020. "Creating a Comprehensive Method for the Evaluation of a Company," Sustainability, MDPI, vol. 12(21), pages 1-23, November.
  24. 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.
  25. 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).
  26. Angilella, Silvia & Doumpos, Michalis & Pappalardo, Maria Rosaria & Zopounidis, Constantin, 2024. "Assessing the performance of banks through an improved sigma-mu multicriteria analysis approach," Omega, Elsevier, vol. 127(C).
  27. Aykut Ekinci & Safa Sen, 2024. "Forecasting Bank Failure in the U.S.: A Cost-Sensitive Approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3161-3179, December.
  28. Kellner, Ralf & Nagl, Maximilian & Rösch, Daniel, 2022. "Opening the black box – Quantile neural networks for loss given default prediction," Journal of Banking & Finance, Elsevier, vol. 134(C).
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