Comparison of various static and dynamic artificial neural networks models in predicting stock prices

In this disquisition، has been paid to comparing the performance of static and dynamics neural network by purpose choosing appropriate model in predicting of Tehran Stock Exchange. The data used in this study consists of daily and interval of time 1388/1/5 to 1390/8/30، that Including 616 observation for in sample and out of sample forecasting. Approximately 90% of these observations (556 data) use to estimate coefficients of the model and the rest of them (60 data) use to forecast out of sample. Models are also employed in this research; two stationary neural network models such as fuzzy neural network (ANFIS) and artificial neural network (ANN) and a dynamic regression neural network model (NNARX). The results of this survey indicate that According to Criteria to calculate the forecast error، among Mean squared error (MSE) and root mean square error (RMSE)، Fuzzy neural network model of static، dynamic regression models، neural networks، and finally static artificial neural network models have lowest prediction error، Respectively.