Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques

Tonatiuh Pena Centeno, Serafín Martínez and Bolanle Abudu
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Serafín Martínez: Banco de México
Bolanle Abudu: University of Essex

A chapter in Computational Methods in Economic Dynamics, 2011, pp 109-131 from Springer

Abstract: 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 classifiers, Bayesian Fisher discriminant and Warped GPs. Our contribution to the field of computational finance is to introduce GPs as a 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: Gaussian Process; Financial Distress; Financial Ratio; Bankruptcy Prediction; Fisher Discriminant Analysis (search for similar items in EconPapers)
Date: 2011
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Working Paper: Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques (2009)
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DOI: 10.1007/978-3-642-16943-4_6

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