An ANN Based New Approach Credit Rating Prediction Model: Evidence from Tehran Stock Exchange

Abstract

Corporate credit rating analysis is one of the most important financial problems, which has attracted lots of research interests in the literature. Recent studies have shown that Artificial Intelligence (AI) methods can achieve better performance than traditional statistical methods. In this paper, Multilayer Perceptrons (MLPs) and multiple statistics methods are used to carry out multi-class credit rating of listed corporations in Tehran Stock Exchange (TSE). To accomplish this goal, a sample of these corporations is randomly selected and financial ratios as independent variables are computed for them. Dependent variable is a discrete variable of four groups as the corporation's bond ratings. The sample is split into training set and test set. With training set sample, the model is constructed through Multiple Discriminant Analysis, and with the remaining, hold out sample of corporations, the model is evaluated. Financial ratios for 126 manufacturing companies quoted in TSE have been used. Two models based on artificial neural networks and multiple discriminant analysis (MDA) are utilized to bond rating of corporations. ANN model achieved 81% and 68% accuracy rates in training and holdout samples, respectively. To evaluate the reliability of the model, the data were examined with multivariate discriminate analysis method. MDA reached 65.3% correct classifications of total initial sample. Results indicate that financial characteristics of corporations are good criterion for their bond rating and the same variables as other countries are significant in Iran too.