Predicting US Banks Bankruptcy: Logit versus Canonical Discriminant Analysis

32 Pages Posted: 25 Aug 2016

See all articles by Zeineb Affes

Zeineb Affes

Université Paris I Panthéon-Sorbonne - Centre d'Economie de la Sorbonne (CES)

Rania Hentati Kaffel

Université Paris I Panthéon-Sorbonne - CES/CNRS

Date Written: August 19, 2016

Abstract

Using a large panel of US banks over the period 2008-2013, this paper proposes an early warning framework to identify bank heading to bankruptcy. We conduct a comparative analysis based on both Canonical Discriminant Analysis and Logit models to examine and to determine the most accurate one. Moreover, we analyze and improve suitability of models by comparing different optimal cut-off score (ROC curve vs theoretical value). The main conclusions are: i) Results vary with cut-off value of score ii) the logistic regression using 0.5 as critical cut-off value outperforms DA model with very high correct classification. On the downside, it produces the highest error type 1 rate iii) ROC curve validation improves the quality of the model by minimizing the error of misclassification of bankrupt banks. Also, it emphasizes better prediction of failure of banks because it delivers in mean the highest error type II.

Keywords: Bankruptcy prediction, Canonical Discriminant Analysis, Logistic regression, Principal Component Analysis, CAMELS, ROC curve, Early-warning system

JEL Classification: G21, G28, G33, C25, C38, C53

Suggested Citation

Affes, Zeineb and Hentati Kaffel, Rania, Predicting US Banks Bankruptcy: Logit versus Canonical Discriminant Analysis (August 19, 2016). 29th Australasian Finance and Banking Conference 2016. Available at SSRN: https://ssrn.com/abstract=2826599 or http://dx.doi.org/10.2139/ssrn.2826599

Zeineb Affes (Contact Author)

Université Paris I Panthéon-Sorbonne - Centre d'Economie de la Sorbonne (CES) ( email )

106-112 Boulevard de l'hopital
106-112 Boulevard de l'Hôpital
Paris Cedex 13, 75647
France

Rania Hentati Kaffel

Université Paris I Panthéon-Sorbonne - CES/CNRS ( email )

106 bv de l'Hôpital
Paris, 75013
France

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