Bank Failure Prediction With Logistic Regression


  • Taha Zaghdoudi Faculty of Economic Sciences and Management of Tunis, UniversitØ Tunis El Manar.


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.

Keywords: Bank Failures; Logit Model

JEL Classifications: G33; C34; C35


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How to Cite

Zaghdoudi, T. (2013). Bank Failure Prediction With Logistic Regression. International Journal of Economics and Financial Issues, 3(2), 537–543. Retrieved from