Bibliographic record
Abstract
This paper investigated how food and beverage (F&B) stocks react to COVID-19. The event study method was applied to four events including the first and second events, were the first COVID-19 positive patients detected in the largest and second-largest economic center of Vietnam. The third and fourth events are related to strong measures to prevent the spread of COVID-19: the nationwide lockdown at the beginning of the second quarter of 2020, and the lockdown of Danang at the beginning of the third quarter of 2020. The results show that the reaction of F&B stock prices to events supports the semi-strong form of efficient market theory. The strong and lasting negative reaction of F&B stocks to the first event can be explained by surprise (first case in Vietnam) and Hochiminh city’s economic engine driving role in the development of Vietnam’s economy. The study finds that heuristic decision-making from nationwide lockdowns (suppression of supply chains during lockdowns) can explain the sub-sector of farming-fishing-ranching products reacted more strongly to the lockdown event in Danang. Based on the research results, this paper provides some policy implications for managers and notes for securities investors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".