Early Warning of Bank Failure in the Arab Region: A Logit Regression Approach

Abstract

The global financial crisis of 2008 taught the biggest lesson of anticipating a financial crisis. The current study aimed to highlight the importance of central banks to build early warning systems to reduce the costs of resolution procedures of weak banks. The data was obtained from published annual reports and balance sheets of 60 commercial banks in the Arab region for the period 2000-2010. Using the logistic regression model to predict the performance of banks or anticipating the possibility of bank failure and build an early warning system, the study identified a few financial indicators such as Capital Adequacy Ratio (CAR); Liquidity (LIQ); Cost to Income Ratio CIR; Return On. Assets (ROA); and Non-Performing Loans (NPL). The impact of the GDP variable on bank`s failure was also determined to capture economic risks. The results showed that financial soundness indicators (FSI) can be used efficiently to predict bank failure, that the variables of ROA and CAR had the greatest impact on the probability of the bank’s survival, while no statistical significance was seen for the GDP variable. The paper recommends the importance of the financial stability and banking supervision departments to build early warning systems. The study would provide useful insights to both household and corporate sectors to look for early warning signs that predict the performance of the banking sector in the Arab countries. The FSIs suggested in the study would also play a prominent role in predicting the success or failure of banks in the Arab region.