Bank Failure Prediction in the Arab Region Using Logistic Regression Model

Interest increased after the last global financial crisis in 2007, as the crisis revealed the challenges facing central banks in dealing with the financial crises arising from the banking sector, and thus exposing the financial positions of the banking sector to high risks that caused the bankruptcy of many commercial banks. This prompted the international financial institutions to develop regulatorily requirements and provide more instruments that reduce the costs of resolving the crisis. As the interest has emerged in the necessity of using the predictive aspect of the bank’s performance, and not waiting for the problem to occur to be dealt with, this approach will lead to reducing the high cost of the solution in terms of early the intervention of the central bank.In this study, we tried to build a logistic regression model to predict the bankruptcy of the bank, based on the data of 40 commercial banks in the Arab region for the period 2005-2015, where the financial soundness indicators were used to predict the bankruptcy of the bank, in addition to the GDP variable in order to capture the impact of economic risks. The study confirmed the findings of previous studies in terms of the ability of the financial soundness indicators used in the (CAMEL) system to predict bank bankruptcy. While the results did not show a significant statistical impact of the variable GDP