Stock Market Prediction of the Saudi Telecommunication Sector Using Univariate Deep Learning Models

Stock market volatility, randomness, and complexity make accurate stock price prediction very elusive, though it is required for logical investment and risk management. This study compares four Deep Learning (DL) models, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and a CNN-LSTM model, to predict the Saudi Telecommunication sector by focusing on the closing price time series. The daily historical closing prices of STC, Mobily, and Zain companies are gathered and preprocessed, involving duplicate removal, feature selection, and Min-Max scaling. Models were trained with MSE loss, whereas validation was done with the RMSE and MAE. The study points toward the ability of deep learning to capture complex nonlinear regression patterns in the ebbs and flows of volatile financial markets. A comparative analysis reveals that the LSTM model yielded the lowest Test RMSE in all cases (Mobily: 1.169705, STC: 0.708495, Zain: 0.27147), therefore, presenting the best overall predictive accuracy. On the other hand, RNN almost always had the highest Test RMSE values (Mobily: 1.688603, STC: 1.143664, Zain: 0.666184), highlighting its limitations. The CNN and CNN-LSTM models showed intermediate performance, with implications for enhanced financial forecasting and decision-making within this specific market segment.

@article{AlQahtani2025,
  title     = {Stock Market Prediction of the Saudi Telecommunication Sector Using Univariate Deep Learning Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Hadi S. AlQahtani and Mohammed J. Alhaddad and Mutasem Jarrah},
  doi       = {10.14569/IJACSA.2025.0160835},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160835}
}