Prediction of bank financial strength ratings: The case of Turkey

Bank financial strength ratings have gained widespread popularity especially after the recent financial turmoil. Rating agencies were criticized because of their ratings and failure to predict the bankruptcy of the banks. Based on this observation, we investigate whether the forecast of the rating of bank's financial strength using publicly available data is consistent with those of the credit rating agency. We use the data of Turkish banks for this investigation. We take a country-specific approach because previous studies found that proxies used for environmental factors (political, economic, and financial risk of the country) did not have any explanatory power and it is hard to find international data for other important factors such as franchise value, concentration, and efficiency. We use two popular multivariate statistical techniques (multiple discriminant analysis and ordered logistic regression) to estimate a suitable model and we compare their performances with those of two mostly used data mining techniques (Support Vector Machine and Artificial Neural Network). Our results suggest that our predictions are consistent with those of Moody's financial strength rating in general.. The important factors in rating are found to be profitability (measured by return on equity), efficient use of resources, and funding the businesses and the households instead of the government that shows efficient placement of the funds.

► The aim of this paper is to investigate the objectivity of bank ratings. ► We used two multivariate statistical and data mining techniques to estimate models. ► Our predictions are consistent with those of Moody's financial strength rating. ► Profitability, efficiency and funding private sectors are important in rating.