Clustering of the stock price using minimum spanning tree

Document Type : Original Article

Authors

Department of Mathematics, Faculty of Basic Sciences, Ayatollah Boroujerdi University, Boroujerd, Iran

Abstract

Due to the increasing activities of individuals and legal entities in the capital market and the transformation of this market into one of the most important economic drivers of any country, it is concluded that more knowledge regarding the selection of shares will undoubtedly lead to higher profitability. In this paper, clustering of stock price time series using the minimum spanning tree algorithm is proposed. The daily closing prices of the shares of the companies listed on the Tehran Stock Exchange from 09/23/2019 to 11/10/2020 are used as the dataset. In the first stage, we form some sub-clusters that include similar companies in terms of price behavior. Then, based on a similarity criterion, sub-clusters are merged until the desired clusters, which contain members with the most similarity, are achieved. The main advantage of the proposed method is that the similarity measures are calculated locally, resulting in lower computational costs compared to other methods. The results indicate that the method can easily perform the clustering process, especially for large datasets, with favorable accuracy.

Keywords

Main Subjects

References

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Soft Computing Journal
Volume 14, Issue 1 - Serial Number 27
September 2025
Pages 72-87

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History

  • Receive Date: 02 September 2023
  • Revise Date: 08 March 2024
  • Accept Date: 02 April 2024

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