Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/83723 
Erscheinungsjahr: 
2009
Schriftenreihe/Nr.: 
Working Papers No. 2009-18
Verlag: 
Banco de México, Ciudad de México
Zusammenfassung: 
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's classifiers, Bayesian Fisher discriminant and Warped GP's. Our contribution to the field of computational 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.
Schlagwörter: 
Bankruptcy prediction
Artificial intelligence
Supervised learning
Gaussian processes
Z-score
JEL: 
C11
C14
C45
Dokumentart: 
Working Paper
Erscheint in der Sammlung:

Datei(en):
Datei
Größe
546.41 kB





Publikationen in EconStor sind urheberrechtlich geschützt.