Stock price prediction based on chaotic hybrid particle swarm optimisation-RBF neural network
by Sainan Wang; Luda Wang; Shouping Gao; Zhi Bai
International Journal of Applied Decision Sciences (IJADS), Vol. 10, No. 2, 2017

Abstract: The stock market is an important part of the capital market, which plays a significant role in optimising capital allocation, financing and increasing the value of assets and other areas. Hence, the correct model for estimating and predicting the stock price has a very important practical significance to provide investors with investment decision reference. In this paper, a novel chaotic hybrid PSO-based RBF neural network model (CHPSO-RBFNN) has been proposed for forecasting the stock price, which can effectively prevent the RBF neural network from the local minimum trap and provide great learning ability. The presented methodology was tested with stock 601998, and the results showed that CHPSO-RBFNN can improve the prediction of accuracy and a high efficient and accurate stock prediction model compared to the traditional RBFNN and PSO-RBFNN methods.

Online publication date: Thu, 18-May-2017

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
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 Applied Decision Sciences (IJADS):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your 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