IDEAS home Printed from https://ideas.repec.org/a/spr/futbus/v11y2025i1d10.1186_s43093-025-00421-0.html
   My bibliography  Save this article

Unveiling market dynamics: a machine and deep learning approach to Egyptian stock prediction

Author

Listed:
  • Ibrahim Eldesouky Fattoh

    (Beni-Suef University)

  • Marwa Maghawry Ibrahim

    (Future University in Egypt)

  • Farid Ali Mousa

    (Beni-Suef University)

Abstract

Accurate stock price forecasting is essential for making smart investing choices. In the context of the Egyptian stock market, this study examines the predictive capabilities of several machine learning and deep learning models for stock price prediction. Five different datasets with historical stock price information from significant Egyptian companies are used in the study methods such as Random Forest, Linear Regression, LSTM, and Bi-LSTM which were employed and evaluated using performance metrics including Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-Squared. Among these, the Bi-LSTM model consistently delivered the most accurate price forecasts due to its superior ability to capture complex patterns and relationships inherent in stock market data. Additionally, statistical models ARIMA and GARCH were implemented for comparative purposes, but the machine learning and deep learning models outperformed these traditional approaches. This study contributes to a deeper understanding of stock price behavior in the Egyptian market, providing valuable insights for financial analysts and investors.

Suggested Citation

  • Ibrahim Eldesouky Fattoh & Marwa Maghawry Ibrahim & Farid Ali Mousa, 2025. "Unveiling market dynamics: a machine and deep learning approach to Egyptian stock prediction," Future Business Journal, Springer, vol. 11(1), pages 1-12, December.
    • Handle: RePEc:spr:futbus:v:11:y:2025:i:1:d:10.1186_s43093-025-00421-0
    DOI: 10.1186/s43093-025-00421-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s43093-025-00421-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1186/s43093-025-00421-0?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.

    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:spr:futbus:v:11:y:2025:i:1:d:10.1186_s43093-025-00421-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.