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Predicting US Banks Bankruptcy: Logit Versus Canonical Discriminant Analysis

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 use random subspace method to compare the classification and prediction of both canonical discriminant analysis and logistic regression models with and without misclassification costs. They have been applied to a large panel of US banks over the period 2008–2013. Results show that model's accuracy have improved in case of more appropriate cut-off value C∗ROC that maximizes the overall correct classification rate under the ROC curve. We also have tested the newly H-measure of classification performance and provided results for different parameters of misclassification costs. Our main conclusions are: (1) The logit model outperforms the CDA one in terms of correct classification rate by using usual cut-off parameters, (2) C∗ROC improves the accuracy of classification in both CDA and logit regression, (3) H-measure and ROC curve validation improve the quality of the model by minimizing the error of misclassification of bankrupt banks. Moreover, it emphasizes better prediction of banks failure because it delivers in average the highest error type II.

Suggested Citation

  • Zeineb Affes & Rania Hentati-Kaffel, 2019. "Predicting US Banks Bankruptcy: Logit Versus Canonical Discriminant Analysis," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03045837, HAL.
    • Handle: RePEc:hal:cesptp:hal-03045837
    DOI: 10.1007/s10614-017-9698-0
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    Citations

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    Cited by:

    1. O. Vasiurenko & V. LYASHENKO, 2020. "Wavelet coherence as a tool for retrospective analysis of bank activities," Economy and Forecasting, Valeriy Heyets, issue 2, pages 43-60.
    2. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    3. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    4. Magdalena Brygała, 2022. "Consumer Bankruptcy Prediction Using Balanced and Imbalanced Data," Risks, MDPI, vol. 10(2), pages 1-13, January.
    5. Manthoulis, Georgios & Doumpos, Michalis & Zopounidis, Constantin & Galariotis, Emilios, 2020. "An ordinal classification framework for bank failure prediction: Methodology and empirical evidence for US banks," European Journal of Operational Research, Elsevier, vol. 282(2), pages 786-801.
    6. Petra Posedel v{S}imovi'c & Davor Horvatic & Edward W. Sun, 2021. "Classifying variety of customer's online engagement for churn prediction with mixed-penalty logistic regression," Papers 2105.07671, arXiv.org, revised Jul 2021.
    7. Petra P. Šimović & Claire Y. T. Chen & Edward W. Sun, 2023. "Classifying the Variety of Customers’ Online Engagement for Churn Prediction with a Mixed-Penalty Logistic Regression," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 451-485, January.
    8. Elena Gregova & Katarina Valaskova & Peter Adamko & Milos Tumpach & Jaroslav Jaros, 2020. "Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods," Sustainability, MDPI, vol. 12(10), pages 1-17, May.
    9. Youssef Zizi & Amine Jamali-Alaoui & Badreddine El Goumi & Mohamed Oudgou & Abdeslam El Moudden, 2021. "An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression," Risks, MDPI, vol. 9(11), pages 1-24, November.
    10. McCarthy, Patrick, 2024. "Predicting trips to health care facilities: A binary logit and receiver operating characteristics (ROC) approach," Research in Transportation Economics, Elsevier, vol. 103(C).
    11. Martina Mokrišová & Jarmila Horváthová, 2023. "Domain Knowledge Features versus LASSO Features in Predicting Risk of Corporate Bankruptcy—DEA Approach," Risks, MDPI, vol. 11(11), pages 1-18, November.

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