Stock price reactions to reopening announcements after China abolished its zero-COVID policy
Bibliographic record
Abstract
Abstract As global economies strive for post-COVID recovery, stock market reactions to reopening announcements have become crucial indicators. Though previous research has extensively focused on COVID’s detrimental impact on stock markets, the effects of reopening remain underexplored. This study provides the first causal analysis of the effect of easing restrictions on Chinese firms’ stock prices following the end of China’s three-year Zero-COVID policy. Utilizing regression-discontinuity design, we find that most relaxed measures had minimal or negative impact. However, stock prices jumped 1.4% immediately after the full reopening announcement on December 26, 2022. Using a difference-in-differences approach, we also note a 1.6% increase in the stock prices of Mainland China firms relative to firms in other districts on the Hong Kong stock market two months post-reopening. Our findings offer key insights for policymakers and contribute significantly to academic discourse on the causal relationship between reopening policies and stock market performance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".