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

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  • Carlos Serrano-Cinca
  • Begoña Gutiérrez-Nieto

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.

Suggested Citation

  • Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto, 2011. "Partial Least Square Discriminant Analysis (PLS-DA) for bankruptcy prediction," Working Papers CEB 11-024, ULB -- Universite Libre de Bruxelles.
    • Handle: RePEc:sol:wpaper:2013/90696
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    References listed on IDEAS

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    Cited by:

    1. Liu, Zhen Jia, 2015. "Estudo cross-country sobre os fatores determinantes da crise financeira bancária," RAE - Revista de Administração de Empresas, FGV-EAESP Escola de Administração de Empresas de São Paulo (Brazil), vol. 55(5), September.
    2. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    3. Maria Jesus Segovia Vargas & Mara del Mar Camacho Miñano, 2018. "Analysis of corporate viability in the pre-bankruptcy proceedings," Contaduría y Administración, Accounting and Management, vol. 63(1), pages 29-30, Enero - M.
    4. Fayçal Mraihi & Inane Kanzari & Mohamed Tahar Rajhi, 2015. "Development of a Prediction Model of Failure in Tunisian Companies: Comparison between Logistic Regression and Support Vector Machines," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(3), pages 184-205.
    5. Yi-Shu Wang & Xue Jiang & Zhen-Jia-Liu, 2016. "Bank Failure Prediction Models for the Developing and Developed Countries: Identifying the Economic Value Added for Predicting Failure," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 6(9), pages 522-533, September.
    6. Fayçal Mraihi, 2016. "Distressed Company Prediction Using Logistic Regression: Tunisian’s Case," Quarterly Journal of Business Studies, Research Academy of Social Sciences, vol. 2(1), pages 34-54.
    7. Maria Jesus Segovia Vargas & Mara del Mar Camacho Miñano, 2018. "Análisis de la viabilidad empresarial en el preconcurso de acreedores," Contaduría y Administración, Accounting and Management, vol. 63(1), pages 27-28, Enero - M.

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    Keywords

    bankruptcy; financial ratios; banking crisis; solvency; data mining; PLS-DA;
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