A Comparative Approach to Financial Clustering Models: (A Study of the Companies Listed on Tehran Stock Exchange)
Publish place: Iranian Journal of Finance، Vol: 6، Issue: 4
Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_IJFIFSA-6-4_002
تاریخ نمایه سازی: 19 مهر 1401
Abstract:
Data mining is known as one of the powerful tools in generating information and knowledge from raw data, and Clustering as one of the standard methods in data mining is a suitable method for grouping data in different clusters that helps to understand and analyze relationships. It is one of the essential issues in the field of investment, so by using stock market clustering, helpful information can be obtained to predict changes in stock prices of different companies and then on how to decide the correct number and shares in the portfolio to private investors and financial professionals' help. The purpose of this study is to cluster the companies listed on the Tehran stock exchange using three methods of K-means Clustering, Hierarchical clustering, and Affinity propagation clustering and compare these three methods with each other. To conduct this research, the adjusted price of ۵۰ listed companies for the period ۲۰۱۹-۰۷-۰۱ to ۲۰۲۰-۰۹-۲۹ has been used. The evaluation results show that the obtained silhouette coefficient for K-means Clustering is higher and, therefore, better than other methods for stock exchange data. In the continuation of the research, calculating the co-integration of stock pairs that have the same co-movement with each other were identified, and finally, clusters were compiled using the t-SNE method.
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Authors
Marziyeh Nourahmadi
Ph.D. Candidate in Financial engineering, Faculty of Economic, Management and Accounting, Yazd University, Yazd, Iran.
Fatemeh Rasti
MSc. in finance, Faculty of Economic, Management and Accounting, Yazd University, Yazd, Iran.
Hojjatollah Sadeqi
Department of Accounting and Finance, Faculty of Humanities and Social Sciences, Yazd University, Yazd, Iran.
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