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

Zeineb Affes (zeineb.affes@univ-paris1.fr) and Rania Hentati-Kaffel (rania.kaffel@univ-paris1.fr)
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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

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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)
Date: 2016-03
New Economics Papers: this item is included in nep-ban, nep-for and nep-sog
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01314553
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Citations: View citations in EconPapers (2)

Published in 2016

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