پیش‌بینی شاخص بورس اوراق بهادار به کمک گراف پدیداری

اصولیان, محمد; صادقی شریف, سید جلال; حسینی اسفیدواجانی, سیدعلی; مبیّن, ندا

doi:10.22034/jse.2024.12247.2151

پیش‌بینی شاخص بورس اوراق بهادار به کمک گراف پدیداری

نوع مقاله : مقاله پژوهشی

نویسندگان

محمد اصولیان ¹

سید جلال صادقی شریف ²

سیدعلی حسینی اسفیدواجانی ³

ندا مبیّن ⁴

¹ استادیار، گروه مدیریت مالی و بیمه، دانشکده مدیریت و حسابداری، دانشگاه شهید بهشتی، تهران، ایران.

² استادیار دانشکده مدیریت، دانشگاه شهید بهشتی تهران، ایران

³ دانشیار دانشکده فیزیک، دانشگاه شهید بهشتی تهران، ایران

⁴ دانشجوی دکتری مدیریت مالی،دانشکده مدیریت، دانشگاه شهید بهشتی تهران، ایران

10.22034/jse.2024.12247.2151

چکیده

پویایی بازار و رفتار تصادفی سری زمانی شاخص در دنیا باعث شده پیش‌بینی روند آن از دغدغه اصلی پژوهشگران و سرمایه‌گذاران بازار سرمایه شود و لذا از مدل‌های مختلف برای پیش‌بینی استفاده گردد . روش‌های تحلیل سری‌های زمانی مبتنی بر شبکه و پیش‌بینی از جمله مدل گراف پدیداری نشان می‌دهد که این روش‌‌ها برای تحلیل سری‌های زمانی مؤثر است. در این پژوهش سعی بر این داشته‌ایم که به کمک سه روش گراف پدیداری به پیش‌بینی شاخص‌ TEDPIX در سال‌های 1392 تا 1403 بپردازیم. این روش‌ها شامل متشابه‌ترین گره، مدل گره‌های متشابه وزن‌دار، و مدل ابداعی پژوهش مدل گراف پدیداری متقاطع می‌باشند.در این راستا سری زمانی شاخص و چند متغیر کمکی را به گراف پدیداری تبدیل کرده و به کمک سه مدل‌ به پیش‌بینی سری‌های زمانی فوق‌الذکر پرداختیم. نتایج نشان داد که عملکرد مدل گره‌های متشابه وزن‌دار و مدل مشابه‌ترین گره بسته به شرایط مختلف گاهی بهتر از دیگری بوده است. با این حال، مدل ابداعی ما، یعنی مدل گراف پدیداری متقاطع و مدل گره‌های متشابه وزن‌دار ، به‌طور کلی در پیش‌بینی شاخص بهترین نتایج را به دست آورده.

کلیدواژه‌ها

پیش‌بینی شاخص

متغیرهای کلان

متغیرهای بازار رقیب

شبکه های پیچیده

گراف پدیداری

موضوعات

بورس اوراق بهادار

عنوان مقاله English

Prediction of the stock market index with the help of visibility graph

نویسندگان English

Mohammad Osoolian ¹

Seyed Jalal Sadeghi sharif ²

Seyed Ali Hosseiny Esfidavajani ³

Neda Mobayen ⁴

¹ Assistant Professor, Department of Financial Management and Insurance, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.

² Assistant Prof., Department of Finance and Accounting, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran,

³ Associate Prof., Department of Physics, Shahid Beheshti University, Tehran, Iran,

⁴ Ph.D. Candidate, Department of Financial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran,

چکیده English

The dynamics of the market and the stochastic behavior of index time series worldwide have made forecasting trends a primary concern for researchers and investors in the capital market. Therefore, various models are used for forecasting. Time series analysis and prediction methods based on networks, including the visibility graph model, have proven effective for time series analysis. In this research, we aimed to predict the TEDPIX index for the years 2013-2024 using three visibility graph-based methods. These methods include the most similar node model, the weighted similar node model, and our proposed method, the cross-visibility graph model. We converted the time series of the index and some auxiliary variables into a visibility graph and predicted the aforementioned time series using these three models. The results showed that the performance of the weighted similar node model and the most similar node model varied depending on different conditions. However, our proposed model, the cross-visibility graph model, and the weighted similar node model generally provided the best results in predicting the index and were more accurate compared to other models.

کلیدواژه‌ها English

Index prediction

Complex networks

Visibility graph

Macro variables

Competitor market Variable

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