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

Peña Tonatiuh, Martínez Serafín and Abudu Bolanle
Authors registered in the RePEc Author Service: Tonatiuh Pena Centeno

No 2009-18, Working Papers from Banco de México

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.

JEL-codes: C11 C14 C45 (search for similar items in EconPapers)
Date: 2009-12
New Economics Papers: this item is included in nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.banxico.org.mx/publications-and-press/ ... -C59BBA55678D%7D.pdf (application/pdf)

Related works:
Chapter: Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques (2011)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bdm:wpaper:2009-18

Access Statistics for this paper

More papers in Working Papers from Banco de México Contact information at EDIRC.
Bibliographic data for series maintained by Subgerencia de desarrollo de sistemas ().