Predicción de bancarrota: Una comparación de técnicas estadísticas y de aprendizaje supervisado para computadora
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.
|Date of creation:||Dec 2009|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://mpra.ub.uni-muenchen.de
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:19560. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ekkehart Schlicht)
If references are entirely missing, you can add them using this form.