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 smallest spanning tree algorithm is suggested. 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.
Kalhori, F., & Hoseini, S. M. (2024). Clustering of the stock price using minimum spanning tree. Soft Computing Journal, (), -. doi: 10.22052/scj.2024.253526.1183
MLA
Faezeh Kalhori; Sayyed Mohammad Hoseini. "Clustering of the stock price using minimum spanning tree". Soft Computing Journal, , , 2024, -. doi: 10.22052/scj.2024.253526.1183
HARVARD
Kalhori, F., Hoseini, S. M. (2024). 'Clustering of the stock price using minimum spanning tree', Soft Computing Journal, (), pp. -. doi: 10.22052/scj.2024.253526.1183
VANCOUVER
Kalhori, F., Hoseini, S. M. Clustering of the stock price using minimum spanning tree. Soft Computing Journal, 2024; (): -. doi: 10.22052/scj.2024.253526.1183
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