Abstract
Predicting stock data with traditional time series analysis has proved to be difficult. An artificial neural network may be more suitable for the task. The reason is that 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. This paper discusses the various conventional analysis methods and neural network methodology to forecast a stock market series. This paper also provides the effect of various topological parameters on the accuracy and training time of neural networks. A topology of neural network is proposed for the prediction of Indian stock market index S&P CNX NIFTY.
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