Accurate prediction of financial time series, such as those generated by stock markets, is a highly challenging task due to the highly nonlinear nature of such series. A novel method of predicting the next day's closing value of a stock market is proposed and empirically validated in the present study. The system uses an adaptive artificial neural network based system to predict the next day's closing value of a stock market index. The proposed system adapts itself to the changing market dynamics with the help of genetic algorithm which tunes the parameters of the neural network at the end of each trading session so that best possible accuracy is obtained. The effectiveness of the proposed system is established by testing on five international stock indices using ten different performance measures. © 2011 Springer-Verlag.
cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@7701e297 ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@1cc3648b Through org.apache.xalan.xsltc.dom.DOMAdapter@5ffdf06d; Conference Code:84611
Dr. Binoy B. Nair, Sai, S. G., Naveen, A. N., Lakshmi, A., Venkatesh, G. S., and Mohandas, V. P., “A GA-artificial neural network hybrid system for financial time series forecasting”, Communications in Computer and Information Science, vol. 147 CCIS, pp. 499-506, 2011.