عنوان مقاله [English]
نویسندگان [English]چکیده [English]
Forecasting stock market price index has always been a challenging task, since it is affected by many economic and non-economic factors; therefore, selecting the best and the most efficient forecasting model is difficult.The time series in the real world, including the stock price index time series, rarely have a pure linear or non-linear structure. The Exponential Smoothing Model, Autoregressive Integrated Moving Average Model, and Nonlinear Autoregressive Neural Network can be used to make forecasts based on time series. In this research, to take advantage of all these models and to reduce forecasting errors, a novel approach was tested by the linear combination of the results of these models.Weights used to combine the results, were determined using Genetic Algorithm and also equal weights. After determining the predictability of time series (using variance ratio test) the proposed hybrid methods were used on a monthly set of Tehran Stock Exchange Price Index (TEPIX). The results showed an improvement in forecasts made by this method with using equal weights compared to each of its constituent models.