A Decision Tree-Based Classification Approach to Rule Extraction for Security Analysis

Document Type

Article

Publication Date

3-2006

Publication Source

International Journal of Information Technology & Decision Making

Abstract

Stock selection rules are extensively utilized as the guideline to construct high performance stock portfolios. However, the predictive performance of the rules developed by some economic experts in the past has decreased dramatically for the current stock market. In this paper, C4.5 decision tree classification method was adopted to construct a model for stock prediction based on the fundamental stock data, from which a set of stock selection rules was derived. The experimental results showed that the generated rules have exceptional predictive performance. Moreover, it also demonstrated that the C4.5 decision tree classification model can work efficiently on the high noise stock data domain.

Inclusive pages

227-240

ISBN/ISSN

0219-6220

Comments

Permission documentation on file.

Publisher

World Scientific Publishing

Volume

5

Peer Reviewed

yes

Issue

1

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