Publication Type:

Journal Article

Source:

Communications in Computer and Information Science, Volume 147 CCIS, Nagpur, Maharashtra, p.499-506 (2011)

ISBN:

9783642205729

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-79955114059&partnerID=40&md5=93b4fc8e72dbb0e7fcf5caead821d62c

Keywords:

Accurate prediction, Artificial Neural Network, Commerce, Finance, financial, Financial data processing, Financial time series, Financial time series forecasting, Forecasting, Genetic algorithms, Highly nonlinear, Hybrid systems, Information technology, International stocks, Market dynamics, Mobile telecommunication systems, Neural networks, Novel methods, Performance measure, Stock market, Stock market index, System use, Time series

Abstract:

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.

Notes:

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

Cite this Research Publication

B.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.