Document Type: Original Article
MSc Graduate of Information Technology Engineering at the Dept. of I.T., Faculty of Industrial &amp; Systems Engineering, Tarbiat Modares University, Tehran, Iran.
Professor of Industrial Engineering at the Dept. of I.E., Faculty of Industrial &amp; Systems Engineering, Tarbiat Modares University,Tehran, Iran.
MSc Graduate of Industrial Engineering at the Dept. of Socio-economic Systems, Faculty of Industrial &amp; Systems Engineering, Tarbiat Modares University, Tehran, Iran.
BSc Graduate of Industrial Engineering, Department of Industrial Engineering and management Systems, Amirkabir University of Technology, Tehran, Iran
Nowadays, in accordance with the recent research, the stock market has a lot of data that is constantly fluctuating. Data analyzing and separating them into distinct groups is time-consuming and difficult for investors and managers. In this study, techniques of community detection are used to simplify stock market analysis. For this purpose, Tehran stock exchange has been selected and after collecting the latest price of transaction data, a correlation network of stock return has been developed. Then, using community detection, cohort companies were identified and it became clear that the stock of the same industry belonged to a common group. Therefore, it can be said that the correlation between stock prices of companies largely depends on the industry in which these companies are active. Hence, it can be concluded that the community detection technique works completely logically and its application will facilitate and accelerate the analysis of stock market data.