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

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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.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 19560.

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Date of creation: Dec 2009
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Handle: RePEc:pra:mprapa:19560

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Related research

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

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