Forecasting Methods Evaluation of Stock Prices and Proposing a Nonlinear Model using Neural Networks

In this paper, we deal with several time series of share prices and daily returns of different companies which are members of Tehran Stock Exchange. Three prediction methods are used for time series forecasting. The first method is based on the linear models (ARIMA) for short-term and long-term forecasting. The second method is based on the nonlinear neural networks model and the third method is a neural networks model with a special structure. It has been shown that the time series generator process of these companies are complex nonlinear mappings and the methods based on the various linear modelling strategies are unable to identify these dynamics. Also, it has been shown by using the conventional structure of the nonlinear neural networks that one can not obtain a satisfactory result for long term forecasting. Finally, it is shown that the proposed structrure provides accurate next step and the long term share prices and daily returns forecasting.