Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques *
Year: 2009
Type: preprint
Abstract: We are interested in forecasting bankruptcies in a probabilistic way. Speciflcally, we compare the classiflcation performance of several statistical and machine-learning techniques, namely discriminant analysis (Altman’s Z-score), logistic regression, least-squares support vector machines and difierent instances of Gaussian processes (GP’s) -that is GP’s classiflers, Bayesian Fisher discriminant and Warped GP’s. Our contribution to the fleld of computational flnance 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
Source: SSRN Electronic Journal
Author: Tonatiuh Pe
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2186497457