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Bank Failure Prediction with Logistic Regression

Taha Zaghdoudi ()

International Journal of Economics and Financial Issues, 2013, vol. 3, issue 2, 537-543

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.

Keywords: Bank Failures; Logit Model (search for similar items in EconPapers)
JEL-codes: G33 C34 C35 (search for similar items in EconPapers)
Date: 2013
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