Bank Performance

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, 53 financial ratios were selected and, consequently, reduced to 28 using the fuzzy Delphi technique. The statistical population included 18 banks listed on TSE and Iran Fara Bourse, which   provided their financial statements during the period of 2011 to 2017. Data were collected from the Codal site based on 28 financial ratios using C4.5 decision tree, AdaBoost, and Naïve Bayes algorithm. According to the findings, the Naïve Bayes algorithm was the optimal predictive model with the accuracy of 88.89%.

The aim of this study was an investigation of the effect of corporate governance on banks’ performance evidence from Indian Banks. This study tested a hypothesis according to the three levels of a model with three groups including the overall, public, and private sectors. This hypothesis focused on the relationship between different variables of the three levels in the new model of bank performance. But, based on the literature review and the basic model, the authors of the present study divided the three main hypotheses into 9 sub hypotheses. The results indicated that the relationship between corporate governance index and dimensions supported the general hypothesis, but at the level of components, all variables did not affect the dependent variable (performance) so that some variables in this level were deleted from the regression equation table. All variables in this study had a positive impact on banks’ performance and the Beta column indicated the coefficient of their impact on banks’ performance in three groups.  JEL Classification: G34, P17, G21