NEURAL NETWORK BASED FORECASTING OF THE MONTHLY CLOSING RETURNS OF NIFTY

Stock price prediction is one of the hot areas in neural network application. One critical step in neural network application is network training. In this paper, we showed a method to forecast the stock index value using neural networks. Predicting the stock market is very difficult since it depends on several known and unknown factors. In recent years, one of the techniques that have been used popularly in this area is artificial neural network. The power of neural network is its ability to model a nonlinear process without a priori knowledge about the nature of the process. The objective of this study is to find out the effect on Closing Return of NIFTY of Industrial Production, Wholesale Price Index, Exchange Rate, and Net Investment by FIIs, Export, Import, and Money Supply by using Neural Network. The data for the study comprises the monthly stock returns of NIFTY, Industrial Production, Wholesale Price Index, Exchange Rate, Net Investment by FIIs, Export, Import, Money Supply. The accuracy measure of prediction is defined in terms of the forecasting error, which is the difference between the actual and predicted value. Experiments illustrate a varying degree of pridictability of the monthly stock returns.