IDEAS home Printed from https://ideas.repec.org/a/ids/ijdmmm/v14y2022i4p335-357.html
   My bibliography  Save this article

Comparison of harmony search derivatives for artificial neural network parameter optimisation: stock price forecasting

Author

Listed:
  • Mehmet Özçalıcı
  • Ayşe Tuğba Dosdoğru
  • Aslı Boru İpek
  • Mustafa Göçken

Abstract

This study has been conducted on forecasting, as accurately as possible, the next day's stock price using harmony search (HS) and its variants [improved harmony search (IHS), global-best harmony search (GHS), self-adaptive harmony search (SAHS), and intelligent tuned harmony Search (ITHS) together with artificial neural network (ANN)]. The advantage of the proposed models are that the useful information in the original stock data is found by input variable selection and simultaneously the most proper number of hidden neurons in hidden layer is discovered to mitigate overfitting/underfitting problem in ANN. The results have shown that forecasts made by HS-ANN, IHS-ANN, GHS-ANN, SAHS-ANN, and ITHS-ANN demonstrate a tendency to achieve hit rates above 89%, which is considerably better than previously proposed forecasting models in literature. Hence, ANN models provide more valuable forecasting results for investors to hedge against potential risk in stock markets.

Suggested Citation

  • Mehmet Özçalıcı & Ayşe Tuğba Dosdoğru & Aslı Boru İpek & Mustafa Göçken, 2022. "Comparison of harmony search derivatives for artificial neural network parameter optimisation: stock price forecasting," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 14(4), pages 335-357.
    • Handle: RePEc:ids:ijdmmm:v:14:y:2022:i:4:p:335-357
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=126664
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    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:ids:ijdmmm:v:14:y:2022:i:4:p:335-357. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=342 .

    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.