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

Document Type : Original Article

Authors

1 PH. D Candidate, Department of Finance, faculty of Management, Islamic Azad University, Central Tehran Branch, Tehran, Iran.

2 Assistant Prof., Department of Finance, Faculty of Management, Islamic Azad University, Central Tehran Branch, Tehran, Iran.

3 Prof., Department of Finance, Faculty of Management, Islamic Azad University, Central Tehran Branch, Tehran, Iran.

Abstract

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%.

Keywords

References

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Iranian Journal of Finance
Volume 3, Issue 1
January 2019
Pages 90-109

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History

  • Receive Date: 22 July 2019
  • Accept Date: 28 December 2019
  • First Publish Date: 28 December 2019

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