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Model combination for credit risk assessment: A stacked generalization approach

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  • Michael Doumpos
  • Constantin Zopounidis

Abstract

The development of credit risk assessment models is often considered within a classification context. Recent studies on the development of classification models have shown that a combination of methods often provides improved classification results compared to a single-method approach. Within this context, this study explores the combination of different classification methods in developing efficient models for credit risk assessment. A variety of methods are considered in the combination, including machine learning approaches and statistical techniques. The results illustrate that combined models can outperform individual models for credit risk analysis. The analysis also covers important issues such as the impact of using different parameters for the combined models, the effect of attribute selection, as well as the effects of combining strong or weak models. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • Michael Doumpos & Constantin Zopounidis, 2007. "Model combination for credit risk assessment: A stacked generalization approach," Annals of Operations Research, Springer, vol. 151(1), pages 289-306, April.
    • Handle: RePEc:spr:annopr:v:151:y:2007:i:1:p:289-306:10.1007/s10479-006-0120-x
    DOI: 10.1007/s10479-006-0120-x
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    Cited by:

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    3. Philippe Jardin, 2023. "Designing topological data to forecast bankruptcy using convolutional neural networks," Annals of Operations Research, Springer, vol. 325(2), pages 1291-1332, June.
    4. Cao Son Tran & Dan Nicolau & Richi Nayak & Peter Verhoeven, 2021. "Modeling Credit Risk: A Category Theory Perspective," JRFM, MDPI, vol. 14(7), pages 1-21, July.
    5. Francisco Salas-Molina & Juan A. Rodriguez-Aguilar & Pablo Díaz-García, 2018. "Selecting cash management models from a multiobjective perspective," Annals of Operations Research, Springer, vol. 261(1), pages 275-288, February.
    6. Górecki Tomasz & Łuczak Maciej, 2017. "Stacked Regression With a Generalization of the Moore-Penrose Pseudoinverse," Statistics in Transition New Series, Polish Statistical Association, vol. 18(3), pages 443-458, September.
    7. Ilyes Abid & Farid Mkaouar & Olfa Kaabia, 2018. "Dynamic analysis of the forecasting bankruptcy under presence of unobserved heterogeneity," Annals of Operations Research, Springer, vol. 262(2), pages 241-256, March.
    8. Koen W. de Bock & Kristof Coussement & Stefan Lessmann, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," Post-Print hal-02863245, HAL.
    9. Jones, Stewart & Johnstone, David & Wilson, Roy, 2015. "An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 72-85.
    10. Ioannis Tsolas, 2015. "Firm credit risk evaluation: a series two-stage DEA modeling framework," Annals of Operations Research, Springer, vol. 233(1), pages 483-500, October.
    11. Yi Liao & Yuxuan Peng & Songlin Shi & Victor Shi & Xiaohong Yu, 2022. "Early box office prediction in China’s film market based on a stacking fusion model," Annals of Operations Research, Springer, vol. 308(1), pages 321-338, January.
    12. Jamal Al Qundus & Kosai Dabbour & Shivam Gupta & Régis Meissonier & Adrian Paschke, 2022. "Wireless sensor network for AI-based flood disaster detection," Annals of Operations Research, Springer, vol. 319(1), pages 697-719, December.
    13. Ilyes Abid & Rim Ayadi & Khaled Guesmi & Farid Mkaouar, 2022. "A new approach to deal with variable selection in neural networks: an application to bankruptcy prediction," Annals of Operations Research, Springer, vol. 313(2), pages 605-623, June.
    14. Ioannis E. Tsolas, 2021. "Firm Credit Scoring: A Series Two-Stage DEA Bootstrapped Approach," JRFM, MDPI, vol. 14(5), pages 1-12, May.
    15. Emilios Galariotis & Christophe Germain & Constantin Zopounidis, 2018. "A combined methodology for the concurrent evaluation of the business, financial and sports performance of football clubs: the case of France," Annals of Operations Research, Springer, vol. 266(1), pages 589-612, July.
    16. Fu-Ling Cai & Xiuwu Liao & Kan-Liang Wang, 2012. "An interactive sorting approach based on the assignment examples of multiple decision makers with different priorities," Annals of Operations Research, Springer, vol. 197(1), pages 87-108, August.
    17. Alina Mihaela Dima & Simona Vasilache, 2016. "Credit Risk modeling for Companies Default Prediction using Neural Networks," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 127-143, September.
    18. Philippe Jardin, 2021. "Forecasting bankruptcy using biclustering and neural network-based ensembles," Annals of Operations Research, Springer, vol. 299(1), pages 531-566, April.
    19. Ioannis Asimakopoulos & Dionysis Lalountas & Costas Siriopoulos, 2008. "The determinants for the survival of firms in the Athens Exchange," Economic Bulletin, Bank of Greece, issue 31, pages 07-30, November.
    20. Ioannidis, Christos & Pasiouras, Fotios & Zopounidis, Constantin, 2010. "Assessing bank soundness with classification techniques," Omega, Elsevier, vol. 38(5), pages 345-357, October.
    21. Tomasz Górecki & Maciej Łuczak, 2017. "Stacked Regression With A Generalization Of The Moore-Penrose Pseudoinverse," Statistics in Transition New Series, Polish Statistical Association, vol. 18(3), pages 443-458, September.

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