خوشه‌بندی قیمت سهام با استفاده از الگوریتم کوچک‌‌ترین درخت پوشا

کلهری, فائزه; حسینی, سیدمحمد

doi:10.22052/scj.2024.253526.1183

خوشه‌بندی قیمت سهام با استفاده از الگوریتم کوچک‌‌ترین درخت پوشا

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه ریاضی، دانشکده علوم پایه، دانشگاه آیت الله بروجردی (ره)، بروجرد، ایران.

10.22052/scj.2024.253526.1183

چکیده

با توجه به فعالیت‌های روزافزون اشخاص حقیقی و حقوقی در بازار سرمایه و تبدیل شدن این بازار به یکی از مهم‌ترین اهرم‌های اقتصادی هر کشور، هرچه دانش در رابطه با انتخاب سهم و سهام‌داری بیشتر باشد، بی‌شک سودآوری بیشتری به دنبال خواهد داشت. در پژوهش حاضر نظر به اهمیت قیمت سهام، خوشه‌بندی آن با استفاده از الگوریتم کوچک‌ترین درخت پوشا پیشنهاد شده است. داده‌های مورد استفاده پژوهش، قیمت بسته شدن روزانه سهام شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران در بازه زمانی 1/7/1398 تا 20/8/1399 است. در این پژوهش در مرحله اول، تعدادی زیرخوشه شامل شرکت‌های مشابه از نظر رفتار قیمتی، تشکیل می‌شود پس از آن بر اساس برخی معیارهای تشابه، زیرخوشه‌ها ادغام و نتیجه مطلوب یعنی داشتن خوشه‌هایی شامل اعضایی با بیشترین مشابهت حاصل می‌شود. از جمله مزایای روش پیشنهادی این است که در این روش معیارهای تشابه به صورت موضعی محاسبه می‌شود و بنابراین محاسبات آن نسبت به سایر روش‌ها کمتر خواهد بود. نتایج این پژوهش نشان می‌دهد که فرآیند خوشه‌بندی مجموعه‌های حجیم به راحتی و با دقتی مطلوب قابل انجام و استفاده است.

کلیدواژه‌ها

موضوعات

یادگیری ماشین

عنوان مقاله [English]

Clustering of the stock price using minimum spanning tree

نویسندگان [English]

Faezeh Kalhori
Sayyed Mohammad Hoseini

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

چکیده [English]

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.

کلیدواژه‌ها [English]

Clustering
spanning tree
stock price
nearest neighbor
Kruskal algorithm

مراجع

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