Partial Least Square Discriminant Analysis for bankruptcy prediction

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.

► Partial Least Square Discriminant Analysis (PLS-DA) for prediction bankruptcy.
► The performance of this technique is compared to the performance of 8 algorithms.
► In terms of accuracy, precision, F-score, Type I and II errors, results are similar.
► The paper also analyzed the scores assigned to each bank by all the techniques.
► PLS-DA results are very close to those obtained by Support Vector Machine.