International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

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 Volume 12, Issue 12 (December 2025), Pages: 266-279

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 Original Research Paper

Prediction of TASI returns using sentiment analysis and hybrid modeling methods: ARIMAX, random forest, and XGBoost

 Author(s): 

 Somaiyah Alalmai *

 Affiliation(s):

 Finance Department, Faculty of Economics and Administration, King Abdulaziz University, Jeddah, Saudi Arabia

 Full text

    Full Text - PDF

 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0001-6623-3171

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2025.12.024

 Abstract

This study examines the predictive relationship between financial news sentiment and the performance of the Saudi stock market, measured by the Tadawul All Share Index (TASI). A news sentiment index is developed using financial headlines from the Saudi Gazette published between March 2017 and March 2025. The FinBERT model, a natural language processing tool designed for financial text, is used to calculate sentiment scores, which are then averaged on a monthly basis. These sentiment measures are combined with key macroeconomic and market variables, including crude oil prices, interest rates, inflation, exchange rates, and trading volume. For prediction, a hybrid modeling framework is applied, integrating ARIMAX, Random Forest, and XGBoost to capture both linear and nonlinear relationships between TASI returns and sentiment. Model performance is evaluated using root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²). The results show that news sentiment and oil price movements have a significant effect on market returns, with important implications for investors, analysts, and policymakers in sentiment-sensitive emerging markets such as Saudi Arabia.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Financial news sentiment, Saudi stock market, Tadawul index, Machine learning models, Market prediction

 Article history

 Received 31 July 2025, Received in revised form 25 November 2025, Accepted 5 December 2025

 Acknowledgment

No Acknowledgment. 

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Alalmai S (2025). Prediction of TASI returns using sentiment analysis and hybrid modeling methods: ARIMAX, random forest, and XGBoost. International Journal of Advanced and Applied Sciences, 12(12): 266-279

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 Figures

  Fig. 1  

 Tables

  Table 1  Table 2  Table 3  Table 4  Table 5  Table 6  Table 7 

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