One-step and multi-step ahead stock prediction using backpropagation neural networks

Forecasting stock price with traditional time series methods has proven to be difficult. An artificial neural network is probably more suitable for this task, since no assumption of a mathematical model has to be made prior to the forecasting process. Furthermore, a neural network has the ability to extract the main influential factors from large sets of data, which is often required for a successful stock prediction task. In this paper, we explore one-step ahead and multi-step ahead predictions and compare with previous work.