Serrano-Cinca, C. and Gutiérrez-Nieto, B. (2013): "Partial Least Square Discriminant Analysis for bankruptcy prediction",Decision Support Systems, Vol 54 (3) pp1245–1255

Discussion Paper: ULB, Universite Libre de Bruxelles, Working Papers CEB, No 11-024.

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   Download (https://dipot.ulb.ac.be/dspace/bitstream/2013/90696/1/wp11024.pdf)