Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/83723
Authors: 
Peña, Tonatiuh
Martínez, Serafín
Abudu, Bolanle
Year of Publication: 
2009
Series/Report no.: 
Working Papers, Banco de México 2009-18
Abstract: 
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.
Subjects: 
Bankruptcy prediction
Artificial intelligence
Supervised learning
Gaussian processes
Z-score
JEL: 
C11
C14
C45
Document Type: 
Working Paper
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