Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange
عنوان مقاله: Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange
شناسه ملی مقاله: JR_IJBDS-8-1_003
منتشر شده در در سال 1395
شناسه ملی مقاله: JR_IJBDS-8-1_003
منتشر شده در در سال 1395
مشخصات نویسندگان مقاله:
Abbas Ali Abounoori
Esmaeil Naderi
Nadiya Gandali Alikhani
Hanieh Mohammadali
خلاصه مقاله:
Abbas Ali Abounoori
Esmaeil Naderi
Nadiya Gandali Alikhani
Hanieh Mohammadali
During the recent decades, neural network models have been focused upon by researchers due to their more real performance and on this basis, different types of these models have been used in forecasting. Now, there is a question that which kind of these models has more explanatory power in forecasting the future processes of the stock. In line with this, the present paper made a comparison between static and dynamic neural network models in forecasting (uninvariable) the return of Tehran Stock Exchange (TSE) index in order to find the best model to be used for forecasting this series. The data were collected daily from ۲۶/۱۱/۲۰۰۹ to ۱۷/۱۰/۲۰۱۴. The models examined in this study included two static models (Adaptive Neuro-Fuzzy Inference Systems "ANFIS" and Multi-layer Feed-forward Neural Network "MFNN") and a dynamic model (nonlinear neural network autoregressive model "NNAR"). The findings showed that based on the Mean Square Error and Root Mean Square Error criteria, ANFIS model had a much higher forecasting ability compared to other models.
کلمات کلیدی: Forecasting, Stock Market, dynamic Neural Network, Static Neural Network
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1232498/