Prediction of Stock Market Index Using Neural Networks: An Empirical Study of BSE

Abstract

Predicting stock data with traditional time series analysis has become one popular research issue. An artificial neural network may be more suitable for the task, because no assumption about a suitable mathematical model has to be made prior to forecasting.  Furthermore, a neural network has the ability to extract useful information from large sets of data, which often is required for a satisfying description of a financial time series. Subsequently an Error Correction Network is defined and implemented for an empirical study. Technical as well as fundamental data are used as input to the network. One-step returns of the BSE stock index and two major stocks of the BSE are predicted using two separate network structures.  Daily predictions are performed on a standard Error Correction Network whereas an extension of the Error Correction Network is used for weekly predictions. The results on the stocks are less convincing; nevertheless the network outperforms the naive strategy.

Keywords: - Prediction of stock, ECN, Backpropagation, Feedforward Neural Networks, Dynamic system.