Incorporating Media Coverage and the Impact of Geopolitical Events for Stock Market Predictions with Machine Learning

Vinayaka Gude () and Daniel Hsiao
Additional contact information
Vinayaka Gude: Department of Management and Entrepreneurship, Elon University, Elon, NC 27244, USA
Daniel Hsiao: Department of Accounting and Finance, East Texas A&M University, Commerce, TX 75428, USA

JRFM, 2025, vol. 18, issue 6, 1-25

Abstract: This paper explores the impact of the Israel–Palestine conflict on the stock performance of U.S. companies and their public positions on the conflict. In an era where corporate positions on geopolitical issues are increasingly scrutinized, understanding the market implications of such statements is critical. This research aims to capture the complex, non-linear relationships between corporate actions, media coverage, and financial outcomes by integrating traditional statistical techniques with advanced machine learning models. To achieve this, we constructed a novel dataset combining public corporate announcements, media sentiment (including headline and article body tone), and philanthropic activities. Using both classification and regression models, we predicted whether companies had affiliations with Israel and then analyzed how these affiliations, combined with other features, affected their stock returns over a 30-day period. Among the models tested, ensemble learning methods such as stacking and boosting achieved the highest classification accuracy, while a Multi-Layer Perceptron (MLP) model proved most effective in forecasting abnormal stock returns. Our findings highlight the growing relevance of machine learning in financial forecasting, particularly in contexts shaped by geopolitical dynamics and public discourse. By demonstrating how sentiment and corporate stance influence investor behavior, this research offers valuable insights for investors, analysts, and corporate decision-makers navigating sensitive political landscapes.

Keywords: forecasting; machine learning; stock returns; classification (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1911-8074/18/6/288/pdf (application/pdf)
https://www.mdpi.com/1911-8074/18/6/288/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:18:y:2025:i:6:p:288-:d:1662076

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().