By Topic

Research of stock index futures prediction model based on rough set and support vector machine

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
$33 $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)
Tao Zhang ; Shandong University of Finance, China ; Ying Sai ; Zheng Yuan

In this paper, a hybrid prediction model based on rough set (RS) and support vector machine (SVM), RSS prediction model, is proposed to explore the stock index futures tendency. In this approach, RS is used for feature vectors selection to reduce the computation complexity of SVM and then the SVM is used to identify stock index futures movement direction. To evaluate the prediction ability of RSS model, we compare its performance with that of neural network model. At the same time, we suggest an investment efficiency formula which is used for decision making. The empirical results reveal that RSS model outperforms other prediction models, implying that RSS model can be used as a viable alternative solution for stock index futures prediction.

Published in:

Granular Computing, 2008. GrC 2008. IEEE International Conference on

Date of Conference:

26-28 Aug. 2008