A novel artificial immune system-based approach for mining associative classification rules with stock trading data
by Mahsa Mahboob Ghodsi; M. Zandieh
International Journal of Innovative Computing and Applications (IJICA), Vol. 8, No. 3, 2017

Abstract: Stock market prediction with high accuracy has always been an interesting subject for most investors and professional analysts. Data mining techniques are providing great aid to extract interesting and hidden knowledge from datasets. Financial data mining tools assist investors in their investment decisions, thereby reducing their investment risks. Associative classification rule mining is a promising approach in data mining that utilises the association rule discovery techniques to construct classification systems, also known as associative classifiers. This paper aims to develop an intelligent transaction system based on associative classification rule mining (ACR) and phenotypic artificial immune system (AIS) which discovers trading rules from numerical indicators. A new fitness function as a different measure of quality for quantitative association is suggested considering interestingness of rules. Based on the empirical studies on the top eight companies in the S&P 500 stocks, observed results demonstrate the superior prediction accuracy over the genetic algorithm based technique and the 'buy and hold' strategy.

Online publication date: Fri, 15-Sep-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 Innovative Computing and Applications (IJICA):
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 subs@inderscience.com