The impact of Trump’s 2024 re-election on international stock markets : a multi-country comparison
Bibliographic record
Abstract
This thesis examines global stock market reactions to Donald Trump’s 2024 re-election, focusing on short and medium-term investor sentiment across seven countries (U.S., Canada, China, Germany, Iran, Saudi Arabia, and the U.K.). Using an event study with windows (-1,+1), (-3,+3), and (-10,+10), it analyzes Abnormal Returns (ARs), Cumulative Abnormal Returns (CARs), and Conditional Value at Risk (CVaR) to assess volatility and downside risk. The findings show heterogeneous effects, strongest in politically sensitive markets such as Iran, while developed markets, including the U.S., remained relatively stable. Results highlight that U.S. elections generate selective rather than global financial impacts, shaped by political alignment and geopolitical exposure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".