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Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques

In: Computational Methods in Economic Dynamics

Author

Listed:
  • Tonatiuh Peña

    (Banco de México)

  • Serafín Martínez

    (Banco de México)

  • Bolanle Abudu

    (University of Essex)

Abstract

We are interested in forecasting bankruptcies in a probabilistic way. Specifically, we compare the classification performance of several statistical and machine-learning techniques, namely discriminant analysis (Altman’s Z-score), logistic regression, least-squares support vector machines and different instances of Gaussian processes (GP’s)—that is GP classifiers, Bayesian Fisher discriminant and Warped GPs. Our contribution to the field of computational finance is to introduce GPs as a competitive probabilistic framework for bankruptcy prediction. Data from the repository of information of the US Federal Deposit Insurance Corporation is used to test the predictions.

Suggested Citation

  • Tonatiuh Peña & Serafín Martínez & Bolanle Abudu, 2011. "Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques," Dynamic Modeling and Econometrics in Economics and Finance, in: Herbert Dawid & Willi Semmler (ed.), Computational Methods in Economic Dynamics, pages 109-131, Springer.
    • Handle: RePEc:spr:dymchp:978-3-642-16943-4_6
    DOI: 10.1007/978-3-642-16943-4_6
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    References listed on IDEAS

    as
    1. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    2. Pedro N. Rodriguez & Arnulfo Rodriguez, 2006. "Understanding and predicting sovereign debt rescheduling: a comparison of the areas under receiver operating characteristic curves," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(7), pages 459-479.
    3. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    4. Cynthia G. McDonald & Linda M. Van De Gucht, 1999. "High-Yield Bond Default And Call Risks," The Review of Economics and Statistics, MIT Press, vol. 81(3), pages 409-419, August.
    5. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    6. Arturo Estrella & Sangkyun Park & Stavros Peristiani, 2000. "Capital ratios as predictors of bank failure," Economic Policy Review, Federal Reserve Bank of New York, issue Jul, pages 33-52.
    7. Varetto, Franco, 1998. "Genetic algorithms applications in the analysis of insolvency risk," Journal of Banking & Finance, Elsevier, vol. 22(10-11), pages 1421-1439, October.
    8. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
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    More about this item

    Keywords

    Gaussian Process; Financial Distress; Financial Ratio; Bankruptcy Prediction; Fisher Discriminant Analysis;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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