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.:
- 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.
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- Martens, David & Vanthienen, Jan & Verbeke, Wouter & Baesens, Bart, 2011. "Performance of classification models from a user perspective," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/274791, Katholieke Universiteit Leuven.
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