Incorporating Media Coverage and the Impact of Geopolitical Events for Stock Market Predictions with Machine Learning
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".