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Predicting failure in the U.S. banking sector: An extreme gradient boosting approach

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Cited by:

  1. 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).
  2. Buckmann, Marcus & Gallego Marquez, Paula & Gimpelewicz, Mariana & Kapadia, Sujit & Rismanchi, Katie, 2023. "The more the merrier? Evidence on the value of multiple requirements in bank regulation," Journal of Banking & Finance, Elsevier, vol. 149(C).
  3. Carmona, Pedro & Dwekat, Aladdin & Mardawi, Zeena, 2022. "No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure," Research in International Business and Finance, Elsevier, vol. 61(C).
  4. Kočenda, Evžen & Iwasaki, Ichiro, 2020. "Bank survival in Central and Eastern Europe," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 860-878.
  5. 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).
  6. Jakub Horak, 2021. "Sanctions as a Catalyst for Russia’s and China’s Balance of Trade: Business Opportunity," JRFM, MDPI, vol. 14(1), pages 1-26, January.
  7. Daria S. Leonteva, 2022. "Using Market Indicators to Refine Estimates of Corporate Bankruptcy Probabilities," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 74-90, December.
  8. Samuel Opoku & Kingsley Opoku Appiah & Prince Gyimah, 2024. "Can We Predict the Financial Distress of Banks in Sub-Saharan Africa?," SAGE Open, , vol. 14(3), pages 21582440241, August.
  9. Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2022. "Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications," Food Policy, Elsevier, vol. 112(C).
  10. Alexandre Momparler & Pedro Carmona & Francisco Climent, 2025. "Catalyzing Sustainable Investment: Revealing ESG Power in Predicting Fund Performance with Machine Learning," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1617-1642, March.
  11. Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
  12. Alexander Ryota Keeley & Ken’ichi Matsumoto & Kenta Tanaka & Yogi Sugiawan & Shunsuke Managi, 2021. "The Impact of Renewable Energy Generation on the Spot Market Price in Germany: Ex-Post Analysis using Boosting Method," The Energy Journal, , vol. 42(1_suppl), pages 1-22, June.
  13. Marcus Buckmann & Andy Haldane & Anne-Caroline Hüser, 2021. "Comparing minds and machines: implications for financial stability," Bank of England working papers 937, Bank of England.
  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. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023. "Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach," Journal of International Economics, Elsevier, vol. 145(C).
  16. 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.
  17. Antonio Dichev & Silvia Zarkova & Petko Angelov, 2025. "Machine Learning as a Tool for Assessment and Management of Fraud Risk in Banking Transactions," JRFM, MDPI, vol. 18(3), pages 1-17, March.
  18. Ren, Tingting & Li, Shaofang & Zhang, Siying, 2024. "Stock market extreme risk prediction based on machine learning: Evidence from the American market," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
  19. Cohen, Gil & Aiche, Avishay, 2023. "Forecasting gold price using machine learning methodologies," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).
  20. Delogu, Marco & Lagravinese, Raffaele & Paolini, Dimitri & Resce, Giuliano, 2024. "Predicting dropout from higher education: Evidence from Italy," Economic Modelling, Elsevier, vol. 130(C).
  21. Li, Jing & Li, Nan & Xia, Tongshui & Guo, Jinjin, 2023. "Textual analysis and detection of financial fraud: Evidence from Chinese manufacturing firms," Economic Modelling, Elsevier, vol. 126(C).
  22. Enes Gul & Efthymia Staiou & Mir Jafar Sadegh Safari & Babak Vaheddoost, 2023. "Enhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region, Türkiye," Sustainability, MDPI, vol. 15(15), pages 1-17, July.
  23. Ionuț Nica & Daniela Blană Alexandru & Simona Liliana Paramon Crăciunescu & Ștefan Ionescu, 2021. "Automated Valuation Modelling: Analysing Mortgage Behavioural Life Profile Models Using Machine Learning Techniques," Sustainability, MDPI, vol. 13(9), pages 1-27, May.
  24. Changju Lee & Sunghoon Lee, 2022. "Exploring the Contributions by Transportation Features to Urban Economy: An Experiment of a Scalable Tree-Boosting Algorithm with Big Data," Land, MDPI, vol. 11(4), pages 1-30, April.
  25. Herrera, Rubén & Climent, Francisco & Carmona, Pedro & Momparler, Alexandre, 2022. "The manipulation of Euribor: An analysis with machine learning classification techniques," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
  26. Richard J. Cebula & Jiay Xu, 2023. "A Brief Survey of Recent Studies of Bank Failures in the U.S," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 76(2), pages 265-274.
  27. Aleksandra Ostrowska, 2023. "Makroekonomiczne determinanty jakości kredytów dla sektora niefinansowego w Polsce," Bank i Kredyt, Narodowy Bank Polski, vol. 54(5), pages 541-556.
  28. Li Yao & He Ni, 2023. "Prediction of patent grant and interpreting the key determinants: an application of interpretable machine learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 4933-4969, September.
  29. 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.
  30. Marcus Buckmann & Paula Gallego Marquez & Mariana Gimpelewicz & Sujit Kapadia & Katie Rismanchi, 2021. "The more the merrier? Evidence from the global financial crisis on the value of multiple requirements in bank regulation," Bank of England working papers 905, Bank of England.
  31. Khudri, Md Mohsan & Hussey, Andrew, 2024. "Breastfeeding and Child Development Outcomes across Early Childhood and Adolescence: Doubly Robust Estimation with Machine Learning," IZA Discussion Papers 17080, Institute of Labor Economics (IZA).
  32. 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.
  33. Xi, Haomeng & Wang, Jizhou, 2024. "Social governance, family happiness, and financial inclusion," Finance Research Letters, Elsevier, vol. 61(C).
  34. Sami Ben Jabeur & Nicolae Stef & Pedro Carmona, 2023. "Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 715-741, February.
  35. Daniel Boos & Nikolaos Karampatsas & Wolfgang Garn & Lampros K. Stergioulas, 2024. "Predicting corporate restructuring and financial distress in banks: The case of the Swiss banking industry," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 47(2), pages 497-533, June.
  36. Manthoulis, Georgios & Doumpos, Michalis & Zopounidis, Constantin & Galariotis, Emilios, 2020. "An ordinal classification framework for bank failure prediction: Methodology and empirical evidence for US banks," European Journal of Operational Research, Elsevier, vol. 282(2), pages 786-801.
  37. Shimomura, Mizue & Keeley, Alexander Ryota & Matsumoto, Ken'ichi & Tanaka, Kenta & Managi, Shunsuke, 2024. "Beyond the merit order effect: Impact of the rapid expansion of renewable energy on electricity market price," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
  38. Silviu-Ionut BABTAN, 2025. "Reforming Real Estate Valuation for Financial Auditors With AI: An In-Depth Exploration of Current Methods and Future Directions," The Audit Financiar journal, Chamber of Financial Auditors of Romania, vol. 23(177), pages 180-196, February.
  39. 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.
  40. Matthew Harding & Gabriel F. R. Vasconcelos, 2022. "Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings?," Papers 2202.04218, arXiv.org.
  41. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
  42. Ismail, I. & Stam, P.J.A. & Portrait, F.R.M. & van Witteloostuijn, A. & Koolman, X., 2024. "Addressing unanticipated interactions in risk equalization: A machine learning approach to modeling medical expenditure risk," Economic Modelling, Elsevier, vol. 130(C).
  43. Zhang, Xuan & Zhao, Yang & Yao, Xiao, 2022. "Forecasting corporate default risk in China," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1054-1070.
  44. Silviu-IonuÈ› BÄ‚BÈšAN, 2024. "AUTOMATED EVALUATION MODELS in real estate market: A comparative analysis between linear regression and XGBoost," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 2(26), pages 1-3.
  45. 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.
  46. 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.
  47. Wookjae Heo & Eunchan Kim & Eun Jin Kwak & John E. Grable, 2024. "Identifying Hidden Factors Associated with Household Emergency Fund Holdings: A Machine Learning Application," Mathematics, MDPI, vol. 12(2), pages 1-38, January.
  48. de Haan, Jakob & Fang, Yi & Jing, Zhongbo, 2020. "Does the risk on banks’ balance sheets predict banking crises? New evidence for developing countries," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 254-268.
  49. 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).
  50. Meng‐Feng Yen & Yu‐Pei Huang & Liang‐Chih Yu & Yueh‐Ling Chen, 2022. "A Two-Dimensional Sentiment Analysis of Online Public Opinion and Future Financial Performance of Publicly Listed Companies," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1677-1698, April.
  51. 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.
  52. Alanis, Emmanuel, 2020. "Is there valuable private information in credit ratings?," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
  53. Jabeur, Sami Ben & Gharib, Cheima & Mefteh-Wali, Salma & Arfi, Wissal Ben, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
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