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 and regression trees (CART) and the newly multivariate adaptive regression splines (MARS) models, in forecasting bankruptcy. Models are tested on a large universe of US banks over a complete market cycle and run under a K-fold cross validation. Then, 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 increases the classification accuracy rate in the training sample (iii) MARS prediction underperforms when the misclassification of the bankrupt banks rate is adopted as a criteria (iv) Finally, results prove 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: 2019-10
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Citations: View citations in EconPapers (3)

Published in Annals of Operations Research, 2019, 281 (1-2), pp.27-64. ⟨10.1007/s10479-018-2845-8⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03045877

DOI: 10.1007/s10479-018-2845-8

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