By Topic

A stock price prediction model by using genetic network programming

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Mori, S. ; Fukuoka Jogakuin High Sch., Japan ; Hirasawa, K. ; Jinglu Hu

A new stock price prediction model is proposed based on genetic network programming (GNP), i.e., an evolutionary computation recently developed. In the proposed prediction model, GNP is applied to searching for an optimal combination of two or more appropriate stock price indices, which is different from a conventional GA or GP based stock price prediction model, where GA or GP is usually used as an optimization technique to search for an optimal value of parameters in the stock price index. In this paper, a combination of several indices is shown to be more effective than a single index, because the most effective index usually differs from one brand to another. A series of simulation studies are carried out to confirm the effectiveness of the proposed new model.

Published in:

SICE 2004 Annual Conference  (Volume:2 )

Date of Conference:

4-6 Aug. 2004