Presenting a Model Based on Evaluation of Performance Banks Listed in Tehran Stock Exchange Using Data Mining Approach
Subject Areas : Financial engineeringelham adakh 1 , arefeh fadaviasghari 2 , Mohammad Ebrahim Mohamad Pourzarandi 3
1 - 1- Department of Industrial Management, faculty of management, Central tehran Branch, Islamic Azad university,Tehran, iran.
2 - Department of Industrial Management, faculty of management, Central tehran Branch, Islamic Azad University, Tehran, iran
3 - Department of Industrial Management, faculty of management ,Central tehran Branch, Islamic Azad University, Tehran, iran
Keywords: Data mining, Tehran Stock Exchange, Performance Evaluation, Financial ratios,
Abstract :
With the growth of private banks , financial and credit institutions, competition for better services has increased. Given the importance of the issue, it is necessary to develop a comprehensive model for evaluating banks. Every organization needs to evaluate its performance to understand its strengths and weaknesses, especially in dynamic environments. The issue of performance appraisal is so widespread that even management experts say: "What cannot be evaluated cannot be managed".Banks, like other organizations in Iran, need performance evaluation to provide more diverse and faster services as well as their development. [6]This study aimed to present a model to evaluate the performance of banks listed in Tehran Stock Exchange using data mining approach. In this research, four data mining models of decision tree C5.0, decision tree C4.5, Naive Bayes classifier, and random forest were implemented and compared to evaluat the performance of banks. To this end, 28 financial ratios (e.g., profitability ratios, liquidity, quality management, asset quality, and capital adequacy) in 18 banks of Tehran Stock Exchange during 2014-2017 were selected as independent variables. In addition, the performance of banks in three categories of acceptable, unacceptable, and moderate was selected as the dependent variable of the study. According to the results, the decision tree C5.0 with the accuracy of 94.4% was the most efficient model proposed in this research.
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