کاربرد مدل ساز اقتصاد سنجی جهت پیشبینی قیمت سهام در بازار سرمایه
محورهای موضوعی : مهندسی مالی
علیرضا سادات نجفی
1
,
سهیلا سردار
2
1 - گروه مدیریت فناوری اطلاعات، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران.
2 - گروه مدیریت صنعتی، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران .
کلید واژه: بازار سرمایه, پیشبینی, مدل ساز اقتصاد سنجی, تحلیل داده‌ها,
چکیده مقاله :
یکی از روشهای مطرح در بررسی علمی بازار سرمایه استفاده از مدل سازهای اقتصاد سنجی می باشد . در پژوهشهای انجام شده اغلب مدلسازهای اقتصاد سنجی محدود، بدون مقایسه و بررسی میزان خطای پیشبینی سایر الگوریتم‌ها ، مورد بررسی قرار گرفته اند . در این پژوهش برای رفع این نقیصه با اجرا و مقایسه روش های مطرح برروی سهم های منتخب و بر اساس پارامترهای ارائه شده کارا ترین الگوریتم مشخص گردیده است. از سوی دیگر اغلب مرتبه جمله خود رگرسیو و مرتبه جمله میانگین متحرک جهت بررسی ها به صورت محدود در نظر گرفته می شود که بر اساس معیار اطلاعات بیزی روش تعیین درجات p و q جهت دستیابی به پاسخ بهینه را ارائه نموده ایم و با مقایسه روش های میانگین متحرک خود رگرسیو ، میانگین متحرک تجمیعی خود رگرسیو ، میانگین متحرک تجمیعی خود رگرسیو فصلی ، میانگین متحرک تجمیعی خود رگرسیو با متغیر توضیحی ،میانگین متحرک تجمیعی خود رگرسیو فصلی با متغیر توضیحی ، مدل خود رگرسیو با واریانس ناهمسانی شرطی تعمیم یافته ، مدل خود رگرسیو نمایی با واریانس ناهمسانی شرطی تعمیم یافته و مدل رگرسیون با خطاهای میانگین متحرک خود رگرسیو در بازار بورس اوراق بهادار تهران مورد بررسی قرار گرفته اند.
Investing in the capital market requires deciding on issues such as selection, timing, price and share buybacks with market research. One of the ways to do this is to use econometric modelers. In the studies performed to compare methods or to present hybrid models, most econometric models have been studied without comparing and predicting the error of prediction error of other algorithms. In this research, the most efficient algorithm for solving this defect is implemented and compared with the proposed methods on selected shares and based on the proposed parameters.On the other hand, often the order of the regression and the mean of the moving average sentence are considered for the finite number of studies, which is based on Bayesian criteria for determining the p and q degrees to obtain the optimal response. This paper compares the methods of self-regressive moving average, cumulative self-regressive moving average, self-regulated seasonal moving average, self-regressive moving average with explanatory variable, cumulative mean self-regression with explanatory variable, self-regression model with variance. Generalized conditional, exponential self-regression model with generalized conditional heterogeneity variance and regression model with moving average self-regression errors for selected symbols of Tehran Stock Exchange.
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