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

ملخص المقالة :

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.

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