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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
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    References listed on IDEAS

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    1. Dhruhi Sheth & Manan Shah, 2023. "Predicting stock market using machine learning: best and accurate way to know future stock prices," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 1-18, February.
    2. Barack Wamkaya Wanjawa, 2016. "Evaluating the Performance of ANN Prediction System at Shanghai Stock Market in the Period 21-Sep-2016 to 11-Oct-2016," Papers 1612.02666, arXiv.org.
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