Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques
Banco de Mexico Working Paper No. 2009-18
35 Pages Posted: 20 Dec 2009
Date Written: December 17, 2009
We are interested in forecasting bankruptcies in a probabilistic way. Specifcally, we compare the classifcation 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.
Keywords: Bankruptcy prediction, Artificial intelligence, Supervised learning, Gaussian processes, Z-score
JEL Classification: C11, C14, C45
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