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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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    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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