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SAUDI ARABIA STOCK PRICES PREDICTION USING MACHINE LEARNING
Keywords:
stock price prediction, machine learning, linear regression, random forest, XGB, AdaBoostDOI:
https://doi.org/10.17654/0972361725034Abstract
The Saudi stock market holds a significant position in the Middle East’s financial landscape, attracting global investors due to its status as the tenth-largest market worldwide by market value. However, forecasting stock prices in this market presents notable challenges owing to its intricate nature and volatility. This study aims to construct a predictive model capable of forecasting Saudi stock prices for the following day, leveraging diverse datasets and algorithms. Specifically, we apply machine learning algorithms for stock price prediction in the Saudi market, focusing on datasets from four prominent companies: Almarai, Saudi Telecommunication Company (STC), Sabic, and Alrajhi Bank. Spanning from 2018 to 2022, our analysis employs various algorithms including linear regression, random forest, XGB, and AdaBoost. Through this research, we aim to assess the accuracy of these models and determine the factors influencing their performance. Our findings consistently demonstrate that linear regression outperforms other models across all datasets, followed by random forest and XGB. Notably, AdaBoost consistently exhibits the lowest accuracy among the models tested. Further data analysis uncovers varying distributions and skewness across datasets, exerting influence on model performance. While machine learning models exhibit promise in stock price prediction, their efficacy is subject to various factors, necessitating ongoing research to refine accuracy and applicability.
Received: February 6, 2025
Accepted: March 21, 2025
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