From the ballot to the bond market: the impact of Donald Trump's return on US treasury yields and inflation expectations
Bibliographic record
Abstract
Purpose This study examines how Donald Trump's re-election on November 6, 2024, influenced US financial markets, focusing on long-term interest rates and inflation expectations. Understanding market responses to political outcomes helps investors manage risk and supports economic forecasting and policy decisions. Design/methodology/approach We use daily data from August 1, 2024 to February 28, 2025 on the 10-Year Treasury Yield (TY10) and the 5-Year Breakeven Inflation Rate (BEI5). Four econometric models are utilized, including an Interrupted Time Series (ITS), Local Projections (LP), Event Study, and Quantile Regression (QR). All models control for key macro-financial factors, including the Economic Policy Uncertainty Index (EPU), the CBOE Volatility Index (VIX), and the US Dollar Index (DXY). Newey-West and bootstrapped standard errors are used to correct for autocorrelation and heteroskedasticity. Findings Results show that TY10 and BEI5 increased gradually after the election. The ITS model showed a trend reversal in which yields and expectations had been rising before the election but began flattening or falling afterward. The LP model found significant increases starting on Day 1, peaking by Day 3, and persisting through Day 7. The event study confirmed a cumulative rise of 8.5 basis points in TY10 and 5 basis points in BEI5. QR revealed stronger effects in lower parts of the distribution. Among controls, DXY had a consistently strong positive effect, while EPU and VIX had more mixed and context-dependent influences. Originality/value This study adds new insight into how financial markets respond to a major political event using high-frequency data and multiple methods.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".