Tariffs and stock returns : the effect of U.S. tariffs and Canadian, Chinese and European retaliatory tariffs in 2018
Bibliographic record
Abstract
In this study, the effect of the ongoing trade war, which started in 2018 between the United States on the one hand and the European Union, China and Canada on the other, on stock returns of publicly traded companies, is analysed. The major events of the trade war in 2018 are included in the study. The study was formalised into two hypotheses. Firstly, that the announcement of tariffs in one market would lead to average abnormal returns, either positive or negative, for companies in that market. Secondly, that the announcement of tariffs in one market would lead to average abnormal returns, either positive or negative, for companies in a foreign market targeted by the tariffs. The results of the study show a statistically significant evidence supporting the hypotheses. The study also shows that stock market reactions cannot only be expected in the markets directly involved in the events but also in other markets. In the study, there are examples of market behaviour both consistent and inconsistent with the efficient market hypothesis, and examples of where a backwards drift in returns follows a market overreaction. \nKeywords: Corporate finance, Fama and French three-factor model, abnormal returns, tariffs, trade war.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".