IDEAS home Printed from https://ideas.repec.org/a/spt/fininv/v2y2013i1f2_1_4.html
   My bibliography  Save this article

Forecasting Movement of the Nigerian Stock Exchange All Share Index using Artificial Neural and Bayesian Networks

Author

Listed:
  • Adetunji Abigail Bola
  • Aderounmu Ganiyu Adesola
  • Omidiora Elijah Olusayo
  • Adigun Abimbola Adebisi

Abstract

This paper presents a study of Artificial Neural Network (ANN) and Bayesian Network (BN) for use in stock index prediction. The data from Nigerian Stock Exchange (NSE) market are applied as a case study. Based on the rescaled range analysis, the neural network was used to capture the relationship in terms of weights between the technical indicators derived from the NSE data and levels of the index. The BayesNet Classifier was based on discretizing the numeric attributes into distinct ranges from where the conditional probability was calculated, stored in the Conditional Probability Table (CPT) and the new instance were classified. The performance evaluation carried out showed results of 59.38% for ANN and 78.13% for BN in terms of predictive power of the networks. The result also showed that Bayesian Network has better performance than ANN when it comes to predicting short period of time; and that useful prediction can be made for All Share index of NSE stock market without the use of extensive market data.

Suggested Citation

  • Adetunji Abigail Bola & Aderounmu Ganiyu Adesola & Omidiora Elijah Olusayo & Adigun Abimbola Adebisi, 2013. "Forecasting Movement of the Nigerian Stock Exchange All Share Index using Artificial Neural and Bayesian Networks," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 2(1), pages 1-4.
    • Handle: RePEc:spt:fininv:v:2:y:2013:i:1:f:2_1_4
    as

    Download full text from publisher

    File URL: http://www.scienpress.com/Upload/JFIA%2fVol%202_1_4.pdf
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:spt:fininv:v:2:y:2013:i:1:f:2_1_4. 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: Eleftherios Spyromitros-Xioufis (email available below). General contact details of provider: http://www.scienpress.com/ .

    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.