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Bank Failure Prediction with Logistic Regression

Author

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  • Taha Zaghdoudi

    (Facult des Sciences Economiques et de Gestion de Tunis)

Abstract

In recent years the economic and financial world is shaken by a wave of financial crisis and resulted in violent bank fairly huge losses. Several authors have focused on the study of the crises in order to develop an early warning model. It is in the same path that our work takes its inspiration. Indeed, we have tried to develop a predictive model of Tunisian bank failures with the contribution of the binary logistic regression method. The specificity of our prediction model is that it takes into account microeconomic indicators of bank failures. The results obtained using our provisional model show that a bank's ability to repay its debt, the coefficient of banking operations, bank profitability per employee and leverage financial ratio has a negative impact on the probability of failure.

Suggested Citation

  • Taha Zaghdoudi, 2013. "Bank Failure Prediction with Logistic Regression," International Journal of Economics and Financial Issues, International Journal of Economics and Financial Issues, vol. 3(2), pages 537-543.
    • Handle: RePEc:eco:journ1:2013-02-26
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    Cited by:

    1. Liu, Zhen Jia, 2015. "Cross-country study on the determinants of bank financial distress," RAE - Revista de Administração de Empresas, FGV-EAESP Escola de Administração de Empresas de São Paulo (Brazil), vol. 55(5), September.
    2. Kim-Hung Pho & Michael McAleer, 2021. "Specification and Estimation of a Logistic Function, with Applications in the Sciences and Social Sciences," Advances in Decision Sciences, Asia University, Taiwan, vol. 25(2), pages 74-104, June.
    3. M. Naresh Kumar & V. Sree Hari Rao, 2015. "A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime," Papers 1502.00882, arXiv.org.
    4. Ahmadian, Azam & Mahsa, Gorji, 2015. "Modeling of Banks ‌Bankruptcy in Iran (Multivariate Statistical Analysis)," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 10(2), pages 1-24, January.
    5. Zhiyong Li & Chen Feng & Ying Tang, 2022. "Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis," Annals of Operations Research, Springer, vol. 315(1), pages 279-315, August.
    6. M. Naresh Kumar & V. Sree Hari Rao, 2015. "A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 83-102, June.
    7. Michael Brusco, 2022. "Logistic Regression via Excel Spreadsheets: Mechanics, Model Selection, and Relative Predictor Importance," INFORMS Transactions on Education, INFORMS, vol. 23(1), pages 1-11, September.
    8. Sami Ben Mim & Aziza Bouzgarrou & Fatma Mabrouk & Jawaher Binsuwadan, 2025. "The Role of Gender Diversity in Preventing Bank Failure: Empirical Evidence from Selected MENA Countries," Sustainability, MDPI, vol. 17(21), pages 1-30, October.
    9. Sreenivasulu Puli & Nagaraju Thota & A. C. V. Subrahmanyam, 2024. "Assessing Machine Learning Techniques for Predicting Banking Crises in India," JRFM, MDPI, vol. 17(4), pages 1-16, March.
    10. Ma. Lim & Hector Lim & Mongkut Piantanakulchai & Francis Uy, 2016. "A household-level flood evacuation decision model in Quezon City, Philippines," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1539-1561, February.
    11. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    12. Malik, Amina & Din, Shahab Ud & Shafiq, Muhammad & Butt, Babar Zaheer & Aziz, Haroon, 2019. "Earning Management and the Likelihood of Financial Distress in Banks — Evidence from Pakistani Commercial Banks," Public Finance Quarterly, Corvinus University of Budapest, vol. 64(2), pages 208-221.
    13. Shiwangi Sethi & Mohinder Singh & Amit Kumar Basantaray, 2025. "A macro–micro framework for predicting bank distress: empirical insights," Indian Economic Review, Springer, vol. 60(1), pages 163-183, June.
    14. Yi-Shu Wang & Xue Jiang & Zhen-Jia-Liu, 2016. "Bank Failure Prediction Models for the Developing and Developed Countries: Identifying the Economic Value Added for Predicting Failure," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 6(9), pages 522-533, September.
    15. Abdus Samad, 2018. "How Early Can Non-Performance Loan Predict Bank Failure? Evidence from US Bank Failure during 2008-2010," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 9(1), pages 90-98, January.
    16. Mohamed M. Khalifa Tailab, 2020. "Using Importance-Performance Matrix Analysis to Evaluate the Financial Performance of American Banks During the Financial Crisis," SAGE Open, , vol. 10(1), pages 21582440209, January.
    17. Ma. Bernadeth B. Lim & Hector R. Lim & Mongkut Piantanakulchai & Francis Aldrine Uy, 2016. "A household-level flood evacuation decision model in Quezon City, Philippines," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1539-1561, February.

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    More about this item

    Keywords

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    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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