Shockwaves of Political Leadership: The Impact of Trump’s Second Presidency on Global Oil Prices
Bibliographic record
Abstract
This study explores how Donald Trump’s 2024 re-election affected Brent crude oil prices using an Interrupted Time Series (ITS) model. Given the importance of U.S. political shifts on global markets, the study tests for a structural break in oil prices following the election. Using daily data from January 2022 to March 2025, it controls for key macro-financial factors: the Economic Policy Uncertainty (EPU) Index, Dow Jones Industrial Average (DJIA), and the 10-year U.S. Treasury Yield. Findings show a significant 5.25% drop in Brent oil prices immediately after Trump’s re-election, indicating initial market uncertainty. However, the trend reversed in the days after, suggesting that investor sentiment adjusted over time. A placebo test found no such effect before the election, strengthening causal claims. Additionally, a Distributed Lag ITS model revealed the decline unfolded gradually. These results echo past research linking political uncertainty to oil price volatility, highlighting the short-term sensitivity of oil markets to leadership shocks. Still, the later price recovery points to longer-term resilience. This research adds to the literature on political impacts on commodity markets, offering useful insights for investors, energy economists, and policymakers navigating politically driven market risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".