Ranking of banks based on CAMELS indicators to predict financial distress by logistic regression and Data Envelopment Analysis

It is very important to choose an efficient monitoring system to assess the financial distress of banks, therefore, one of the most important monitoring systems used to assess the financial distress of banks is the CAMELS monitoring system. Which includes six indicators; Capital adequacy, asset quality, management quality, revenue quality, liquidity, market risk sensitivity. Therefore, in this study, the criterion of financial helplessness of banks is CAMELS indicators. Initially, 17 banks listed on the Tehran Stock Exchange in the fiscal year 1399 were ranked and divided into healthy and helpless financial groups by CAMELS indicators. Then, models, Data Envelopment Analysis and logistic Regression were used to predict the financial distress of banks. Then, with the pairwise comparison test (T), the prediction accuracy of both models was investigated. In logistic regression method, binary model with ForwardlR method was used. And in data envelopment analysis method, SBM model with different application was used. The results showed that the overall accuracy of the logistic regression model is higher than the data envelopment analysis model in assessing financial distress and also the CAMELS monitoring system can be a good assessor for banks' financial distress.