Partial Least Square Discriminant Analysis (PLS-DA) 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.
|Date of creation:||Jun 2011|
|Publication status:||Published by:|
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- 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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