برآورد ریسک و بازده سهام با استفاده از رویکرد ترکیبی برنامه¬ریزی ترجیحات فازی لگاریتمی و شبکه¬های عصبی
محورهای موضوعی :
ابوالفضل دهقانی فیروزآبادی
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داریوش فرید
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وجیهه عندلیب اردکانی
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کلید واژه: بازار سهام, سبد سهام, برنامه¬ریزی ترجیحات فازی لگاریتمی, شبکه¬های عصبی.,
چکیده مقاله :
هدف: پژوهش حاضر با هدف شناسایی متغیرهای مؤثر بر انتخاب سبد سهام و نیز اولویت¬بندی این متغیرها و نیز برآورد ریسک و بازده سهام نمونه با استفاده از الگوریتم شبکه¬های عصبی انجام شده است. ضرورت: مسأله انتخاب سبد سهام همواره یکی از موضوعات جذاب و کاربردی در مسائل مالی و بازارهای مالی بوده است. در راستای برطرف کردن معایب موجود درپژوهش¬های مربوط به انتخاب سبد سهام، ایده به¬کارگیری روش برنامه¬ریزی ترجیحات فازی لگاریتمی برای تحلیل عوامل مؤثر بر انتخاب سبد سهام و استفاده از شبکه¬های عصبی جهت برآورد ریسک و بازده تقویت می¬شود. روش شناسی: پژوهش حاضر رویکردی ترکیبی و جدید برای انتخاب سبد سهام ارائه می¬دهد که شامل دو مرحله است: در مرحله اول از طریق مصاحبه با خبرگان و نیز بررسی مدارک و اسناد موجود، 6 معیار اصلی انتخاب سبد بهینه سهام را شناسایی نموده و با استفاده از رویکرد برنامه¬ریزی ترجیحات فازی لگاریتمی، وزن این معیارها تعیین می¬شود و در مرحله دوم ریسک و بازده سهام با استفاده از الگوریتم شبکه¬های عصبی پیش¬بینی می¬شود. یافته‌ها: یافته¬ها نشان می¬دهد معیارهای سودآوری، کارایی و ریسک به ترتیب مهمترین معیارها در انتخاب سبد بهینه سهام می¬باشد. همچنین شبکه¬عصبی طراحی شده توانسته است به خوبی بازده و ریسک سهام را برازش نماید.
The present research aims to identify the influential variables on stock portfolio selection, prioritize these variables, and estimate the risk and return of sample stocks using neural network algorithms. Rationale: Stock portfolio selection has always been an intriguing and practical issue in financial matters and financial markets. In order to address the existing drawbacks in research related to stock portfolio selection, the idea of employing the fuzzy logarithmic preference programming method for analyzing factors affecting stock portfolio selection and utilizing neural networks for risk and return estimation is reinforced. Methodology: The present research offers a novel combined approach for stock portfolio selection consisting of two stages: In the first stage, by conducting interviews with experts and examining available documents and records, six primary criteria for selecting an optimal stock portfolio are identified. Using the fuzzy logarithmic preference programming approach, the weights of these criteria are determined. In the second stage, the risk and return of stocks are predicted using neural network algorithms. Conclusion: The findings indicate that profitability, efficiency, and risk are the most important criteria in selecting an optimal stock portfolio, respectively. Additionally, the designed neural network successfully fitted the returns and risks of stocks.
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