IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v18y2025i5p281-d1658949.html
   My bibliography  Save this article

A Hybrid Forecasting Model for Stock Price Prediction: The Case of Iranian Listed Companies

Author

Listed:
  • Fatemeh Keyvani

    (Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad 9177948951, Iran)

  • Farzaneh Nassirzadeh

    (Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad 9177948951, Iran)

  • Davood Askarany

    (Department of Accounting and Finance, Business School, The University of Auckland, Auckland 1010, New Zealand)

  • Ehsan Khansalar

    (Accounting and Finance, Faculty of Business and Law, De Montfort University, Building No. 12, Dubai Internet City 501870, United Arab Emirates)

Abstract

This paper introduces advanced computational methods for stock price prediction, integrating Fast Recurrent Neural Networks (FastRNN) with meta-heuristic algorithms such as the Horse Herd Optimization Algorithm (HOA) and the Spotted Hyena Optimizer (SHO). By challenging the Efficient Market Hypothesis (EMH) and Random Walk Hypothesis, our research demonstrates the effectiveness of these hybrid models in semi-strong or weak-form efficient markets. The study leverages data from five listed Iranian companies (2011–2021) and 25 factors encompassing technical, fundamental, and economic considerations. Our findings highlight the superior accuracy of the FastRNN optimised by HOA, SHO, and a Generative Adversarial Network (GAN) in forecasting stock prices compared to conventional FastRNN models. This research contributes to the multidisciplinary field of computational economics, emphasising advanced computing capabilities to address complex economic problems through innovative econometrics, optimisation, and machine learning approaches.

Suggested Citation

  • Fatemeh Keyvani & Farzaneh Nassirzadeh & Davood Askarany & Ehsan Khansalar, 2025. "A Hybrid Forecasting Model for Stock Price Prediction: The Case of Iranian Listed Companies," JRFM, MDPI, vol. 18(5), pages 1-22, May.
  • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:5:p:281-:d:1658949
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/18/5/281/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1911-8074/18/5/281/
    Download Restriction: no
    ---><---

    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:gam:jjrfmx:v:18:y:2025:i:5:p:281-:d:1658949. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.