Publication Title
Journal of Risk and Financial Management
Document Type
Article
Abstract/Description
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.
Department
Marketing and Business Analytics
DOI
10.3390/jrfm18060288
ISSN
1911-8074
Date
5-1-2025
Citation Information
Gude, Vinayaka and Hsiao, Daniel, "Incorporating Media Coverage and the Impact of Geopolitical Events for Stock Market Predictions with Machine Learning" (2025). Faculty Publications. 10.
https://lair.etamu.edu/busi-faculty-publications/10
