Capability Comparison of the Models based on Long Memory and Dynamic Neural Network Models in Forecasting the Stock Return Index in Tehran Stock Exchange

Subject Areas : Financial Knowledge of Securities Analysis

اکبر کمیجانی ¹ (ندارد)
اسماعیل نادری ² (مسئول مکاتبات)

Received: 2012-02-20 Accepted : 2012-05-09 Published : 2012-12-15

Keywords: Forecasting, Stock Market, ARFIMA, NNAR,

Abstract :

The aim of this study is to introduce an efficient nonlinear model for predicting thereturn of Tehran Stock Exchange (TSE) Price index. For this purpose, the daily timeseries of price index from Farvardin 1388 to Aban 1390 is used. This study includes616 observations; 90% of which used for estimating coefficients and the remaining 60observation are deduced for out of sample forecasting. By comparing the results of anonlinear dynamic artificial neural network (NNAR) and a nonlinear regression model(autoregressive fractional integration moving average «ARFIMA»), we found thatNNAR models have better performance in out of sample forecasting based on meansquare error criteria (MSE) and root mean square error criteria (RMSE) than thenonlinear regression models (ARFIMA).