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Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis

Zeineb Affes () and Rania Hentati-Kaffel ()
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Zeineb Affes: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique
Rania Hentati-Kaffel: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique

Authors registered in the RePEc Author Service: Rania HENTATI-KAFFEL

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

Abstract: Using a large panel of US banks over the period 2008-2013, this paper proposes an early-warning framework to identify bank leading to bankruptcy. We conduct a comparative analysis based on both Canonical Discriminant Analysis and Logit models to examine and to determine the most accurate of these models. 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 an average of correct classification equal to 96.22%. However, it produces the highest error type 1 rate 42.67%, iii) ROC curve validation improves the quality of the model by minimizing the error of misclassification of bankrupt banks: only 4.42% in average and exhibiting 0% in both 2012 and 2013. Also, it emphasizes better prediction of failure of banks because it delivers in mean the highest error type II 8.43%.

Keywords: Bankruptcy prediction; Canonical Discriminant Analysis; Logistic regression; CAMELS; ROC curve; Early-warning system (search for similar items in EconPapers)
Date: 2016-02
New Economics Papers: this item is included in nep-ban and nep-rmg
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01281948
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Citations: View citations in EconPapers (9)

Published in 2016

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