Partial Least Square Discriminant Analysis (PLS-DA) for bankruptcy prediction
AbstractThis paper uses Partial Least Square Discriminant Analysis (PLS-DA) for the prediction of the 2008 USA banking crisis. PLS regression transforms a set of correlated explanatory variables into a new set of uncorrelated variables, which is appropriate in the presence of multicollinearity. PLS-DA performs a PLS regression with a dichotomous dependent variable. The performance of this technique is compared to the performance of 8 algorithms widely used in bankruptcy prediction. In terms of accuracy, precision, F-score, Type I error and Type II error, results are similar; no algorithm outperforms the others. Behind performance, each algorithm assigns a score to each bank and classifies it as solvent or failed. These results have been analyzed by means of contingency tables, correlations, cluster analysis and reduction dimensionality techniques. PLS-DA results are very close to those obtained by Linear Discriminant Analysis and Support Vector Machine.
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Bibliographic InfoPaper provided by ULB -- Universite Libre de Bruxelles in its series Working Papers CEB with number 11-024.
Length: 23 p.
Date of creation: Jun 2011
Date of revision:
Publication status: Published by:
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bankruptcy; financial ratios; banking crisis; solvency; data mining; PLS-DA;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-07-13 (All new papers)
- NEP-CFN-2011-07-13 (Corporate Finance)
- NEP-FOR-2011-07-13 (Forecasting)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
- Lo, Andrew W., 1986. "Logit versus discriminant analysis : A specification test and application to corporate bankruptcies," Journal of Econometrics, Elsevier, vol. 31(2), pages 151-178, March.
- du Jardin, Philippe & Séverin, Eric, 2011. "Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model," MPRA Paper 44262, University Library of Munich, Germany.
- Foster D.P. & Stine R.A., 2004. "Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 303-313, January.
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