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Forecast bankruptcy using a blend of clustering and MARS model: case of US banks

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zeineb Affes & Rania Hentati-Kaffel, 2019. "Forecast bankruptcy using a blend of clustering and MARS model: case of US banks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03045877, HAL.
    • Handle: RePEc:hal:cesptp:hal-03045877
    DOI: 10.1007/s10479-018-2845-8
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    Citations

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    Cited by:

    1. Marek Vochozka & Jaromir Vrbka & Petr Suler, 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    2. Jinghai Shao & Sovan Mitra & Andreas Karathanasopoulos, 2022. "Optimal feedback control of stock prices under credit risk dynamics," Annals of Operations Research, Springer, vol. 313(2), pages 1285-1318, June.
    3. P. K. Viswanathan & Suresh Srinivasan & N. Hariharan, 2020. "Predicting Financial Health of Banks for Investor Guidance Using Machine Learning Algorithms," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 19(2), pages 226-261, August.

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