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Predicción de bancarrota: Una comparación de técnicas estadísticas y de aprendizaje supervisado para computadora

Author

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  • Pena Centeno, Tonatiuh
  • Martinez Jaramillo, Serafin
  • Abudu, Bolanle

Abstract

We are interested in forecasting bankruptcies in a probabilistic way. Specifcally, we com- pare 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's classifiers, Bayesian Fisher discriminant and Warped GP's. Our contribution to the field of computa- tional finance is to introduce GP's as a potentially 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

  • Pena Centeno, Tonatiuh & Martinez Jaramillo, Serafin & Abudu, Bolanle, 2009. "Predicción de bancarrota: Una comparación de técnicas estadísticas y de aprendizaje supervisado para computadora," MPRA Paper 19560, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:19560
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    More about this item

    Keywords

    Bankruptcy prediction; Artificial intelligence; Supervised learning; Gaussian processes; Z-score.;

    JEL classification:

    • 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
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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