Comparison of Some Data Mining Models in Forecast of Performance of Banks Accepted in Tehran Stock Exchange Market

In order to survive in the modern world, organizations must be equipped with the mechanisms that not only maintain their competitive advantage, but also result in their progress and improvement. Prediction of banks’ performances is an important issue, and a poor performance in banks may primarily lead to their bankruptcy, thereby affecting national economics. The bank performance prediction model uses scientific and systematic approaches to diagnose the financial operations of institutes. According to a precise and strict evaluation, the model can detect the weakness of institutions in advance and provide early warning signals to related financial governments. In the present study, we have used three data mining models to predict the future performance of the banks accepted in Tehran Stock Exchange (TSE) and Iran Fara Bourse. Initially, ۵۳ financial ratios were selected and, consequently, reduced to ۲۸ using the fuzzy Delphi technique. The statistical population included ۱۸ banks listed on TSE and Iran Fara Bourse, which   provided their financial statements during the period of ۲۰۱۱ to ۲۰۱۷. Data were collected from the Codal site based on ۲۸ financial ratios using C۴.۵ decision tree, AdaBoost, and Naïve Bayes algorithm. According to the findings, the Naïve Bayes algorithm was the optimal predictive model with the accuracy of ۸۸.۸۹%.

Keywords:

Bank Performance , Data mining , Financial Ratios , Tehran Stock Exchange