Efficient prediction of stock market indices using adaptive neural network

Abstract

This paper presents a neural network model for financial time series prediction of leading Indian stock market indices. Financial time-series data are usually non-stationary and volatile in nature. This model employs Multilayer Feedforward (MLFF) network with Backpropagation (BP) learning. In this article we discuss the modeling of the Indian stock market (price index) data using artificial neural network (ANN). We study the efficacy of ANN in modeling the Bombay Stock Exchange (BSE), Reliance and Oracle data set on closing values. The root mean square error (RMSE) and mean absolute error (MAE) are chosen as indicators of performance of the network. The backpropagation learning algorithm selects a training example, makes a forward and a backward pass, and then repeats until algorithm converges satisfying a pre-specified mean squared error value. The results show the potential of the system as a tool for making stock price prediction. © 2012 Springer India Pvt. Ltd.