Trade uncertainty: Impacts of Trump tariff risk on technology and energy stock markets
Bibliographic record
Abstract
This paper investigates how global technology and energy markets are exposed to tariff risks during the Trump first and second term presidency in the context of international trade uncertainty. Using the multivariable simultaneous quantile regression and data from January 1, 2017 to May 30, 2025, the paper examines daily and monthly responses of technology and energy stock markets to tariff risks using the US Trade Policy Uncertainty Index (TPU_US) and World Trade Uncertainty Index (WTUI). The sample covers the global market, Australia, Canada, China, France, India, Japan, Sweden, Taiwan, the United Kingdom (UK), and the United States (US). The results indicate that trade risk exerts significant daily impacts on both technology and energy markets, with its varying effects across different market conditions. Specifically, in most markets, the impact transitions are from negative in lower quantiles reflecting bearish or unstable market conditions to positive in higher quantiles, associated with bullish market phases. This pattern suggests that, during economic downturns, US trade policy uncertainty increases perceived risk and depresses returns in both technology and energy sectors. However, under favourable market conditions, such uncertainty may create opportunities for certain assets within these sectors to serve as effective hedges, potentially enhancing their attractiveness to investors during bull markets. This study timely contributes to the literature on the asymmetric effects of tariff risks on technology and energy stock markets at the global and national levels. Our findings offer practical implications for policy makers and investment practitioners that investing in technology and energy sectors can hedge against trade policy risks under bullish market conditions.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".