Partial Least Square Discriminant Analysis (PLS-DA) for bankruptcy prediction

Carlos Serrano-Cinca () and Begoña Gutiérrez-Nieto

No 11-024, Working Papers CEB from ULB -- Universite Libre de Bruxelles

Abstract: This 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.

Keywords: bankruptcy; financial ratios; banking crisis; solvency; data mining; PLS-DA (search for similar items in EconPapers)
Pages: 23 p.
Date: 2011-06
New Economics Papers: this item is included in nep-cfn and nep-for
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Citations: View citations in EconPapers (7)

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