Forecast Bankruptcy Using a Blend of Clustering and MARS Model - Case of US Banks

35 Pages Posted: 22 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 16, 2016

Abstract

In this paper, we compare the performance of two non-parametric methods of classification, Regression Trees (CART) and the newly Multivariate Adaptive Regression Splines (MARS) models, in forecasting bankruptcy. Models are implemented on a large universe of US banks over a complete market cycle and running under a K-Fold Cross validation. A hybrid model which combines K-means clustering and MARS is tested as well. Our findings highlight that i) Either in training or testing sample, MARS provides, in average, better correct classification rate than CART model ii) Hybrid approach significantly enhances the classification accuracy rate for both the training and the testing samples iii) MARS prediction underperforms when the misclassification rate is adopted as a criteria iv) Results proves that Non-parametric models are more suitable for bank failure prediction than the corresponding Logit model.

Keywords: Bankruptcy prediction, MARS, CART, K-means, Early-Warning System

JEL Classification: C14, C25, C38, C53, G17, G21, G28, G33

Suggested Citation

Affes, Zeineb and Hentati Kaffel, Rania, Forecast Bankruptcy Using a Blend of Clustering and MARS Model - Case of US Banks (August 16, 2016). 29th Australasian Finance and Banking Conference 2016. Available at SSRN: https://ssrn.com/abstract=2824492 or http://dx.doi.org/10.2139/ssrn.2824492

Zeineb Affes

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 (Contact Author)

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

106 bv de l'Hôpital
Paris, 75013
France

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