Forecast bankruptcy using a blend of clustering and MARS model - Case of US banks

Zeineb Affes () and Rania Hentati-Kaffel ()
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Zeineb Affes: Centre d'Economie de la Sorbonne, https://centredeconomiesorbonne.univ-paris1.fr
Rania Hentati-Kaffel: Centre d'Economie de la Sorbonne, https://centredeconomiesorbonne.univ-paris1.fr

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

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 (search for similar items in EconPapers)
JEL-codes: C14 C25 C38 C53 G17 G21 G28 G33 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2016-03
New Economics Papers: this item is included in nep-ban and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:16026

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