مدل سازی در مهندسی

مدل سازی در مهندسی

مدل بهینه‌سازی سبد سهام مبتنی بر تحلیل صنایع با استفاده از نظریه دمپستر-شفر در بازار سرمایه ایران

نوع مقاله : مقاله صنایع

نویسندگان
1 گروه مدیریت مالی و حسابداری، دانشکده مدیریت و حسابداری، دانشکدگان فارابی، دانشگاه تهران، قم، ایران
2 گروه مهندسی صنایع، دانشکده مهندسی کامپیوتر و صنایع، دانشگاه صنعتی بیرجند، بیرجند، ایران
چکیده
بهینه‌سازی سبد سهام یکی از مسائل مهم در حوزه علوم مالی و سرمایه‌گذاری است. بیشتر مطالعات انجام شده در این زمینه به شناسایی شرکت‌های برتر و تعیین وزن بهینه شرکت‌های منتخب پرداخته‌اند. این در حالیست که شناسایی صنایعی با پتانسیل بازدهی بالا و تعیین وزن آن‌ها نیز می‌تواند به شناسایی سهام با بازدهی مطلوب و در نتیجه بهبود عملکرد سبد سهام کمک کند. هدف این پژوهش، ارائه یک مدل جدید برای رتبه‌بندی و شناسایی صنایعی با پتانسیل رشد بالا در بازار سرمایه ایران است.
عوامل مختلفی بر شناسایی عملکرد آتی صنایع تاثیر گذارند. یکی از مهم‌ترین آن‌ها احساسات سرمایه‌گذاران است که از طریق شاخص‌های تکنیکی محاسبه می‌شوند. نظریه شواهد دمپستر-شفر به عنوان یکی از شیوه‌های تصمیم‌گیری چندمعیاره، از نظریه احتمال بیزی بهره می‌برد و قابلیت کاهش پیچیدگی و عدم قطعیت موجود در بازارهای مالی را دارد. لذا در این پژوهش از نظریه دمپستر-شفر برای رتبه‌بندی و شناسایی صنایع برتر به کمک شاخص‌های تکنیکی استفاده خواهد شد.
نتایج حاصل از بررسی شش نیمسال (از ابتدای سال ۱۴۰۰ تا پایان سال ۱۴۰۲) جهت رتبه‌بندی ده صنعت شامل: سیمان، انبوه‌سازی، دارو، آهن و فولاد، فلزات گرانبهای غیرآهن، محصولات کانی غیرفلزی، محصولات شیمیایی، محصولات غذایی، قطعات خودرو و بیمه در بازار سرمایه ایران، حاکی از قابلیت پیش‌بینی عملکرد آینده این صنایع با استفاده از مدل پیشنهادی است. به سرمایه‌گذاران نهادی و خرد پیشنهاد می‌گردد، ابتدا از مدل پیشنهادی جهت شناسایی صنایع برتر استفاده نموده و سپس با توجه به صنایع منتخب، سبد سهام را تشکیل دهند.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Industry-Based Portfolio Optimization Model by Dempster-Shafer Theory in the Iranian Capital Market

نویسندگان English

Alireza Saranj 1
Mohammad Nadiri 1
Javad Tayebi 2
Seyed Mohammad Reza Kazemi 1
1 Department of Finance and Accounting Faculty of Management and Accounting, Farabi School, University of Tehran, Qom, Iran
2 Department of Industrial Engineering, Faculty of Industrial and Computer Engineering, Birjand University of Technology, Birjand, Iran
چکیده English

Optimizing a stock portfolio is one of the important issues in the field of finance and investment. Most studies conducted in this area have focused on identifying top companies and determining the optimal weight of selected companies. However, identifying industries with high return potential and determining their weights can also help in recognizing stocks with desirable returns, thereby improving portfolio performance. The aim of this paper is to provide a new model for ranking and identifying industries with high growth potential in the Iranian capital market. Various factors influence the future performance identification of industries. One of the most significant of these is investor sentiment, which is assessed through technical indicators. The Dempster-Shafer evidence theory, as one of the multi-criteria decision-making methods, utilizes Bayesian probability theory and can reduce the complexity and uncertainty present in financial markets. Therefore, this research will use Dempster-Shafer theory to rank and identify top industries with the help of technical indicators. The results obtained from examining six half-years (from March 21, 2021, to March 20, 2024) for ranking ten industries, including cement, housing, pharmaceuticals, iron and steel, precious non-ferrous metals, non-metallic mineral products, chemicals, food products, automotive parts, and insurance in the Iranian capital market, indicate the predictive capability of the future performance of these industries using the proposed model. It is recommended that both institutional and individual investors first use the proposed model to identify the top industries and then form their stock portfolios based on the selected industries.

کلیدواژه‌ها English

Industry ranking
Technical indicators
Dempster-Shafer theory
Portfolio optimization
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دوره 23، شماره 83
زمستان 1404
صفحه 237-254

  • تاریخ دریافت 18 تیر 1403
  • تاریخ بازنگری 08 بهمن 1403
  • تاریخ پذیرش 10 بهمن 1403