IDEAS home Printed from https://ideas.repec.org/a/eee/riibaf/v81y2026ics0275531925004568.html

Forecasting corporate bankruptcy in imbalanced datasets using a new hybrid machine learning approach

Author

Listed:
  • Veganzones, David
  • Séverin, Eric
  • Ben Jabeur, Sami

Abstract

Bankruptcy prediction is a challenging task. Researchers face the problem of class imbalances, because the number of bankrupt firms is much lower than the number of non-bankrupt firms. Resampling methods, which modify data distributions, are commonly employed to deal with this problem. The authors therefore propose a new, alternate, classifier-level solution that combines the adaptive boosting (AdaBoost) algorithm and support vector machine (SVM) methods: Diverse AdaBoostSVM. A comparison of the performance of Diverse AdaBoostSVM, with resampling methods in imbalanced datasets reveal that at moderate degrees of imbalance and in large training sets Diverse AdaBoostSVM is an effective alternative method of predicting bankruptcy, particularly with regard to mid-term forecast horizons.

Suggested Citation

  • Veganzones, David & Séverin, Eric & Ben Jabeur, Sami, 2026. "Forecasting corporate bankruptcy in imbalanced datasets using a new hybrid machine learning approach," Research in International Business and Finance, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:riibaf:v:81:y:2026:i:c:s0275531925004568
    DOI: 10.1016/j.ribaf.2025.103200
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0275531925004568
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ribaf.2025.103200?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Xing, Jin & Chi, Guotai & Pan, Ancheng, 2024. "Instance-dependent misclassification cost-sensitive learning for default prediction," Research in International Business and Finance, Elsevier, vol. 69(C).
    2. John Muschelli, 2020. "ROC and AUC with a Binary Predictor: a Potentially Misleading Metric," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 696-708, October.
    3. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    4. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    5. Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2025. "Bankruptcy prediction using machine learning and Shapley additive explanations," Review of Quantitative Finance and Accounting, Springer, vol. 65(1), pages 107-148, July.
    6. Philippe Jardin, 2025. "Designing Ensemble-Based Models Using Neural Networks and Temporal Financial Profiles to Forecast Firms’ Financial Failure," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 149-209, January.
    7. Xavier Brédart & Eric Séverin & David Veganzones, 2021. "Human resources and corporate failure prediction modeling: Evidence from Belgium," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1325-1341, November.
    8. 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.
    9. du Jardin, Philippe & Séverin, Eric, 2011. "Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model," MPRA Paper 44262, University Library of Munich, Germany.
    10. Talayeh Razzaghi & Ilya Safro & Joseph Ewing & Ehsan Sadrfaridpour & John D. Scott, 2019. "Predictive models for bariatric surgery risks with imbalanced medical datasets," Annals of Operations Research, Springer, vol. 280(1), pages 1-18, September.
    11. Andreas Charitou & Evi Neophytou & Chris Charalambous, 2004. "Predicting corporate failure: empirical evidence for the UK," European Accounting Review, Taylor & Francis Journals, vol. 13(3), pages 465-497.
    12. Eric Severin & David Veganzones & Souhir Chlibi, 2023. "Influence of earnings management on forecasting corporate failure," Post-Print hal-04335071, HAL.
    13. Neda Abdelhamid & Arun Padmavathy & David Peebles & Fadi Thabtah & Daymond Goulder-Horobin, 2020. "Data Imbalance in Autism Pre-Diagnosis Classification Systems: An Experimental Study," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-16, March.
    14. Philippe Jardin & David Veganzones & Eric Séverin, 2019. "Forecasting Corporate Bankruptcy Using Accrual-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 7-43, June.
    15. Kapetanios, George, 2009. "Testing for strict stationarity in financial variables," Journal of Banking & Finance, Elsevier, vol. 33(12), pages 2346-2362, December.
    16. Yang Liu & Qingguo Zeng & Bobo Li & Lili Ma & Joaquín Ordieres‐Meré, 2022. "Anticipating financial distress of high‐tech startups in the European Union: A machine learning approach for imbalanced samples," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1131-1155, September.
    17. 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).
    18. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    19. Eric Severin & David Veganzones, 2021. "Can earnings management information improve bankruptcy prediction models?," Post-Print hal-04331249, HAL.
    20. Tuong Le & Minh Thanh Vo & Bay Vo & Mi Young Lee & Sung Wook Baik, 2019. "A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction," Complexity, Hindawi, vol. 2019, pages 1-12, August.
    21. Sigrist, Fabio & Hirnschall, Christoph, 2019. "Grabit: Gradient tree-boosted Tobit models for default prediction," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 177-192.
    22. Eric Séverin & David Veganzones, 2021. "Can earnings management information improve bankruptcy prediction models?," Annals of Operations Research, Springer, vol. 306(1), pages 247-272, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fatemeh Keivani & Germ`a Coenders & Ge`orgia Escaram'is, 2026. "Adapting Altman's bankruptcy prediction model to the compositional data methodology," Papers 2603.24215, arXiv.org, revised May 2026.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2025. "Bankruptcy prediction using machine learning and Shapley additive explanations," Review of Quantitative Finance and Accounting, Springer, vol. 65(1), pages 107-148, July.
    2. Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2023. "Bankruptcy prediction using machine learning and Shapley additive explanations," Post-Print hal-04223161, HAL.
    3. 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.
    4. Weiyu Wang & Maria João Guedes, 2025. "Firm failure prediction for small and medium-sized enterprises and new ventures," Review of Managerial Science, Springer, vol. 19(7), pages 1949-1982, July.
    5. Dina Ait Lahcen & Nour-Eddin Amghar, 2025. "Econometric modeling for proactive risk management of financial failure in Moroccan SMEs: a stepwise logistic regression approach in python," Future Business Journal, Springer, vol. 11(1), pages 1-25, December.
    6. Lenka Papíková & Mário Papík, 2022. "Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 254-281, October.
    7. Roy, Tonoy & Bhuiyan, Rubaiyat Ahsan & Ahmed, Sarwar Uddin & Abdullah, Mohammad, 2024. "Geopolitical conflict and firm bankruptcy risk," Finance Research Letters, Elsevier, vol. 68(C).
    8. Insu Choi & Wonje Yun & Woo Chang Kim, 2025. "Improving data efficiency for analyzing global exchange rate fluctuations based on nonlinear causal network-based clustering," Annals of Operations Research, Springer, vol. 352(3), pages 745-780, September.
    9. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    10. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, December.
    11. Lisa Crosato & Josep Domenech & Caterina Liberati, 2024. "Websites’ data: a new asset for enhancing credit risk modeling," Annals of Operations Research, Springer, vol. 342(3), pages 1671-1686, November.
    12. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2022. "Corporate Bankruptcy Prediction Using Machine Learning Methodologies with a Focus on Sequential Data," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1231-1249, March.
    13. Rahman, Md Jahidur & Zhu, Hongtao, 2024. "Detecting accounting fraud in family firms: Evidence from machine learning approaches," Advances in accounting, Elsevier, vol. 64(C).
    14. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    15. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    16. 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).
    17. Ma, Xuejiao & Che, Tianqi & Jiang, Qichuan, 2025. "A three-stage prediction model for firm default risk: An integration of text sentiment analysis," Omega, Elsevier, vol. 131(C).
    18. Jarmila Horváthová & Martina Mokrišová & Martin Bača, 2023. "Bankruptcy Prediction for Sustainability of Businesses: The Application of Graph Theoretical Modeling," Mathematics, MDPI, vol. 11(24), pages 1-20, December.
    19. Abinzano, Isabel & Gonzalez-Urteaga, Ana & Muga, Luis & Sanchez, Santiago, 2020. "Performance of default-risk measures: the sample matters," Journal of Banking & Finance, Elsevier, vol. 120(C).
    20. Yufei Cao & Abdulmajeed Mawhan H. Alfadhli & Mohammad Jaradat & Ramona Lile & Mihaela Gadoiu & Mariana Banuta & Daniela Mihai & Malik Shahzad Shabbir, 2024. "The impact of accounting practices on financial sustainability: A study of external block-holders and institutional ownership," Review of Managerial Science, Springer, vol. 18(7), pages 1945-1961, July.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:riibaf:v:81:y:2026:i:c:s0275531925004568. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ribaf .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.