Forecasting stock prices using hybrid non-stationary time series model with ERNN

In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed a hybrid non-stationary model with Elman's Recurrent Neural Networks (ERNN). The proposed model is non-stationary in trend component with lagged variable, average of all past observations and ERNN. This model can capture both linear and non-linear structures in time series. The non-linear structure is capture by ERNN. We derive the expression for the h-step ahead minimum mean square error (MMSE) forecast for the proposed model. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model and it is found that the proposed approach has the best forecasting accuracy.