IDEAS home Printed from https://ideas.repec.org/a/wsi/ijitdm/v05y2006i01ns0219622006001824.html
   My bibliography  Save this article

A Decision Tree-Based Classification Approach To Rule Extraction For Security Analysis

Author

Listed:
  • N. REN

    (Department of Computer Science, Southern Illinois University Carbondale, Mailcode 4511, Carbondale, IL, 62901-4511, USA)

  • M. ZARGHAM

    (Department of Computer Science, Southern Illinois University Carbondale, Mailcode 4511, Carbondale, IL, 62901-4511, USA)

  • S. RAHIMI

    (Department of Computer Science, Southern Illinois University Carbondale, Mailcode 4511, Carbondale, IL, 62901-4511, USA)

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.

Suggested Citation

  • N. Ren & M. Zargham & S. Rahimi, 2006. "A Decision Tree-Based Classification Approach To Rule Extraction For Security Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 227-240.
    • Handle: RePEc:wsi:ijitdm:v:05:y:2006:i:01:n:s0219622006001824
    DOI: 10.1142/S0219622006001824
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219622006001824
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0219622006001824?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Iram Parvez & Jianjian Shen & Ishitaq Hassan & Nannan Zhang, 2021. "Generation of Hydro Energy by Using Data Mining Algorithm for Cascaded Hydropower Plant," Energies, MDPI, vol. 14(2), pages 1-28, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:ijitdm:v:05:y:2006:i:01:n:s0219622006001824. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijitdm/ijitdm.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.