روشی ترکیبی جهت پیش‌بینی قیمت سهام در بازار بورس ایران با بهره گیری از یادگیری عمیق بهینه شده

چکیده مقاله :

پیش‌بینی دقیق قیمت سهام همواره یکی از چالش‌های اساسی در بازارهای مالی بوده است و توسعه‌ی مدل‌های هوشمند می‌تواند نقش مؤثری در تصمیم‌گیری سرمایه‌گذاران ایفا کند. در این پژوهش، یک چارچوب ترکیبی مبتنی بر یادگیری عمیق برای پیش‌بینی قیمت سهام در بازار سرمایه ایران ارائه شده است. روش پیشنهادی از شبکه عصبی کانولوشنی به‌عنوان هسته‌ی اصلی یادگیری و از الگوریتم فراابتکاری شاهین هریس برای بهینه‌سازی وزن‌ها و پارامترهای مدل بهره می‌گیرد. هدف از این ترکیب، افزایش دقت پیش‌بینی و کاهش پیچیدگی محاسباتی از طریق یادگیری خودکار ویژگی‌ها در لایه‌های میانی شبکه است. داده‌های مورداستفاده شامل اطلاعات روزانه‌ی سهام شرکت بهمن خودرو از تاریخ ۱۸/۰۱/۱۳۸۰ تا ۲۳/۱۲/۱۳۹۹ بوده و متغیرهایی مانند تعداد تراکنش‌ها، حجم معاملات، ارزش، و قیمت‌های دیروز، اولین، آخرین، پایانی، کمترین و بیشترین را در بر می‌گیرد. نتایج شبیه‌سازی‌ها نشان می‌دهد که مدل پیشنهادیCNN-HHO  نسبت به روش‌های متداول مبتنی بر شبکه‌های عصبی و الگوریتم‌های فراابتکاری، عملکرد بهتری داشته و مقدار خطای MSE آن به طور محسوسی کمتر است. به‌طورکلی، نتایج این تحقیق نشان می‌دهد که ترکیب CNN  با الگوریتم HHO می‌تواند به‌عنوان یک رویکرد هوشمند، دقیق و کارا برای پیش‌بینی سری‌های زمانی مالی مورداستفاده قرار گیرد و راهکار مؤثری برای تصمیم‌گیری آگاهانه‌تر در بازار بورس فراهم سازد.

چکیده انگلیسی :

Accurate stock price prediction has consistently been one of the fundamental challenges in financial markets, and the development of intelligent models can play a significant role in supporting investors’ decision-making processes. In this study, a hybrid deep learning–based framework is proposed for stock price prediction in the Iranian capital market. The proposed approach employs a Convolutional Neural Network (CNN) as the core learning architecture and integrates the Harris Hawks Optimization (HHO) algorithm as a metaheuristic strategy to optimize the model’s weights and parameters. The primary objective of this integration is to enhance prediction accuracy while reducing computational complexity through automatic feature extraction within the intermediate layers of the network. The dataset used in this research consists of daily stock information of Bahman Khodro Company from 18/01/1380 to 23/12/1399 (Persian calendar), including variables such as the number of transactions, trading volume, trading value, and prices (previous, opening, closing, final, lowest, and highest). Simulation results demonstrate that the proposed CNN-HHO model outperforms conventional neural network–based and metaheuristic-based methods, achieving a significantly lower Mean Squared Error (MSE). Overall, the findings indicate that the integration of CNN with the HHO algorithm can serve as an intelligent, accurate, and efficient approach for financial time-series forecasting, providing an effective tool for more informed decision-making in the stock market.

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