2026
doi: 10.12720/jait.17.4.696-710
Deep Learning-Based Stock Price Forecasting for the Saudi Telecommunication Sector: A Comparative Evaluation with Baseline Models
Hadi S. AlQahtani1,*
,
Mohammed J. Alhaddad1 ,
Mutasem Jarrah2
- 1Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
- 2Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Applied Science University, Amman, Jordan
- Manuscript receivedJuly 28, 2025
- revisedAugust 18, 2025
- acceptedDecember 9, 2025
- publishedApril 16, 2026
Abstract
Accurately predicting stock prices is a long-standing challenge due to the volatility and nonlinear structure of financial markets. To address this, the present study conducts a comprehensive evaluation of Deep Learning (DL) architectures for univariate stock price forecasting within the Saudi telecommunications sector. Four DL models—Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and a hybrid CNN-LSTM—are benchmarked against the Naïve, SMA-5, and Autoregressive Integrated Moving Average (ARIMA) models using five years of daily closing prices for Saudi Telecom Company (STC), Mobily, and Zain. All models performances were assessed using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) on inverse-scaled predictions. The findings clearly show that DL models capture complex temporal dependencies more effectively than traditional baselines. LSTM consistently achieved the lowest test RMSE across all datasets (Mobily: 1.169705; STC: 0.708495; Zain: 0.271470), confirming its superior predictive capability. CNN and CNN-LSTM delivered competitive accuracy, while RNN exhibited the weakest performance. Overall, the results demonstrate the strong potential of deep learning—particularly LSTM—for improving short-term stock price prediction and advancing data-driven financial analytics in the Saudi stock market.
Keywords
- Deep Learning (DL)
- stock market
- prediction
- models
- regression
- time series
How to Cite
Hadi S. AlQahtani, Mohammed J. Alhaddad, and Mutasem Jarrah, "Deep Learning-Based Stock Price Forecasting for the Saudi Telecommunication Sector: A Comparative Evaluation with Baseline Models," Journal of Advances in Information Technology, vol. 17, no. 4, pp. 696-710, 2026. https://doi.org/10.12720/jait.17.4.696-710
Copyright & License
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
