The Impact of COVID-19 on Stock Market Returns in Vietnam
Bibliographic record
Abstract
This paper studies the impacts of COVID-19 on the performance of the Vietnamese Stock Market—a rapidly growing emerging market in a country that has to date successfully controlled the disease outbreak. The study uses a random-effect model (REM) on panel data of stock returns of 733 listed companies on both HOSE (the Ho Chi Minh Stock Exchange) and HNX (the Hanoi Stock Exchange) from 2 January 2020 to 13 December 2020. The study shows that the number of daily COVID-19 confirmed cases in Vietnam has a negative impact on stock returns of listed companies in the market. The impacts were more severe for the pre-lockdown and second-wave period, compared to impact for the lockdown period. The impacts also differed across sectors, with the financial sector being the most affected. With significant government control and influence over the bank-dominated financial system, the financial sector was expected to absorb some of the negative shocks hitting the real sector. Such expectations were reflected in the stock market movement during the pandemic.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".