Predicción de bancarrota: Una comparación de técnicas estadísticas y de aprendizaje supervisado para computadora
AbstractWe 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 InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 19560.
Date of creation: Dec 2009
Date of revision:
Bankruptcy prediction; Artificial intelligence; Supervised learning; Gaussian processes; Z-score.;
Find related papers by 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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