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Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model

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  • du Jardin, Philippe
  • Séverin, Eric
Abstract
The aim of this study is to show how a Kohonen map can be used to increase the forecasting horizon of a financial failure model. Indeed, most prediction models fail to forecast accurately the occurrence of failure beyond one year, and their accuracy tends to fall as the prediction horizon recedes. So we propose a new way of using a Kohonen map to improve model reliability. Our results demonstrate that the generalization error achieved with a Kohonen map remains stable over the period studied, unlike that of other methods, such as discriminant analysis, logistic regression, neural networks and survival analysis, traditionally used for this kind of task.

Suggested Citation

  • 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.
    • Handle: RePEc:pra:mprapa:44262
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    References listed on IDEAS

    as
    1. du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.
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    Cited by:

    1. Ángel Beade & Manuel Rodríguez & José Santos, 2024. "Business failure prediction models with high and stable predictive power over time using genetic programming," Operational Research, Springer, vol. 24(3), pages 1-41, September.
    2. 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).
    3. Błażej Prusak, 2018. "Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries," IJFS, MDPI, vol. 6(3), pages 1-28, June.
    4. Joon Hyung Cho & Jungpyo Lee & So Young Sohn, 2021. "Predicting future technological convergence patterns based on machine learning using link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5413-5429, July.
    5. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    6. Tomasz Korol, 2020. "Assessment of Trajectories of Non-bankrupt and Bankrupt Enterprises," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1113-1135.
    7. 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.
    8. Ling‐Jing Kao & Chih‐Chou Chiu & Hung‐Jui Wang & Chang Yu Ko, 2021. "Prediction of remaining time on site for e‐commerce users: A SOM and long short‐term memory study," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1274-1290, November.
    9. Nurul Izzaty Hasanah Azhar & Norziana Lokman & Md. Mahmudul Alam & Jamaliah Said, 2021. "Factors determining Z-score and corporate failure in Malaysian companies," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 21(3), pages 370-386.
    10. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, December.
    11. Man Ha & Christopher Gan & Cuong Nguyen & Patricia Anthony, 2021. "Self-Organising (Kohonen) Maps for the Vietnam Banking Industry," JRFM, MDPI, vol. 14(10), pages 1-18, October.
    12. Marialuisa Restaino & Marco Bisogno, 2019. "A Business Failure Index Using Rank Transformation," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 11(1), pages 56-65, January.
    13. Miroslav Svatoš & Markéta Chovancová, 2013. "The influence of subsidies on the economic performance of Czech farms in the regions," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 61(4), pages 1137-1144.
    14. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    15. Yehuda Pivin & Yossi Yagil, 2024. "The relationship between institutional investors’ holding in public firms and the level of corporate solvency," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 28(4), pages 1237-1256, December.
    16. Laura Fabregat-Aibar & Maria-Teresa Sorrosal-Forradellas & Glòria Barberà-Mariné & Antonio Terceño, 2021. "Can Artificial Neural Networks Predict the Survival Capacity of Mutual Funds? Evidence from Spain," Mathematics, MDPI, vol. 9(6), pages 1-10, March.
    17. Richard Chamboko & Jorge Miguel Bravo, 2020. "A Multi-State Approach to Modelling Intermediate Events and Multiple Mortgage Loan Outcomes," Risks, MDPI, vol. 8(2), pages 1-29, June.
    18. 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.
    19. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto, 2011. "Partial Least Square Discriminant Analysis (PLS-DA) for bankruptcy prediction," Working Papers CEB 11-024, ULB -- Universite Libre de Bruxelles.
    20. 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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    More about this item

    Keywords

    Financial failure prediction; Self-organizing map; Forecasting horizon;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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