The US Reciprocal Tariff Announcement: An Analysis of Market Reactions
Bibliographic record
Abstract
This paper examines the high-frequency impact of tariff rhetoric on financial markets, a topic largely unexplored in existing literature. Unlike previous studies that focus on the long-term, macroeconomic effects of enacted trade policies, our research utilizes a novel, sentiment-based proxy variable for non-legislated tariff announcements. We demonstrate that political communication itself—not just formal policy changes—is a potent source of investor uncertainty and market volatility. Our analysis, employing a multi-model framework including VAR and EGARCH models, reveals several key findings. We find that trade-related shocks contribute significantly to market volatility by altering investor expectations and increasing perceived risk. A key discovery is a unique unidirectional causality where shocks to the S&P 500 preceded changes in our tariff variable, suggesting that market movements can influence policy rhetoric. Furthermore, our EGARCH analysis uncovers distinct volatility characteristics across asset classes, including an atypical positive asymmetry in the Chinese CSI 300. These results collectively provide robust empirical evidence that tariff rhetoric has a measurable and significant impact on asset prices and disproportionately increases market volatility, highlighting the need for policymakers to consider the financial market implications of their public statements.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".