Prediction of Stock Market Index Using Neural Networks: An Empirical Study of BSE
R. Lakshman Naik, B M Manjula, Dadi Ramesh, B Satyanarayana Murthy, Ssvn Sarma · European Journal of Business and Management · 2012
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.