multi-step-ahead prediction of stock price using a new architecture of neural networks

modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of different stocks prices. several factors, such as input variables, preparing data sets, network architectures and training procedures, have huge impact on the accuracy of the neural network prediction. the purpose of this paper is to predict multi-step-ahead prices of the stock market and derive the method, based on recurrent neural networks (rnn), real-time recurrent learning (rtrl) networks and nonlinear autoregressive model process with exogenous input (narx). this model is trained and tested by tehran securities exchange data.