A hybrid approach to forecast stock market index Online publication date: Wed, 15-Jul-2015
by Gurbinder Kaur; Joydip Dhar; Rangan K. Guha
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Vol. 5, No. 2, 2015
Abstract: The forecasting of stock market problem from the available data is quite often of uncertain nature, hence the stock market prediction is a very challenging and difficult task. In this paper, we have investigated the predictability of stock market of Bombay Stock Exchange (BSE30), Hang Sang China Stock Index (HS), Japan Stock Index (NIKKEI) and Taiwan Weighted Index (TWI) with adaptive network-based fuzzy inference system (ANFIS) combined with subtractive clustering technique. In this process, we compared stock markets with variable numbers of data clusters. Optimised subtractive clustering is used to cluster the data and create fuzzy membership functions. Finally, a hybrid learning algorithm has been used to combine least square method and back propagation gradient-decent method for training the fuzzy inference system. This paper represents a state of the art for ANFIS application to forecast stock market index.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Artificial Intelligence and Soft Computing (IJAISC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook