نوع مقاله : مقاله پژوهشی
نویسندگان
دانشکده اقتصاد، دانشگاه خوارزمی، تهران، ایران.
چکیده
هدف اصلی این تحقیق استفاده از یک مدل بهینه‌سازی پرتفوی مبتنی بر پیش‌بینی برای انتخاب پرتفوی سهام گروه‌های وابسته به نفت در بازار بورس تهران است. برای این منظور، ابتدا با استفاده از داده‌های خوشه‌بندی شده بازار سهام و مبتنی بر رهیافت داده‌کاوی، سهام فرآورده‌های نفتی و صنایع شیمیایی پیش‌بینی شده است. سپس، با استفاده از عوامل موثر بر تغییرات شاخص هر گروه مانند قیمت نفت خام، نرخ ارز، نرخ بهره جهانی، قیمت جهانی طلا و شاخص S&P500 شاخص هر صنعت با استفاده از الگوریتم‌های شبکه عصبی MLP و RBF تخمین زده شده و در نهایت با مقایسه عملکرد هر یک از الگوریتم‌ها، بهترین الگوریتم برای پیش‌بینی رفتار شاخص هر صنعت شناسایی شده است. در ادامه با استفاده از الگوریتم‌های خوشه-بندی K-Means، SOM و FCM شرکت‌های موجود در این دوصنعت از لحاظ نسبت‌های مالی خوشه‌بندی شده و با بهترین الگوریتم سهام مناسب از هرگروه شناسایی شده است. نتایج تحقیق بیانگر آن است که الگوریتم MLP ازدقت بالاتری برخوردار است. همچنین الگوریتم FCM بهترین خوشه‌ها را تولید می‌کند. نتایج تجربی نشان می‌دهد، سهام پتروشیمی سپاهان و خارگ در کوتاه‌مدت و پتروشیمی خارگ و فناوران و پالایشگاه نفت تهران بیشترین بازده را در پرتفوی در افق میان‌مدت و بلندمدت دارد.
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