Research on the Influence of the COVID-19 Pandemic on Pharmaceutical Stock Markets in China —Based on Granger Causality Test
Bibliographic record
Abstract
The World Health Organization (WHO) declared the coronavirus disease, which broke out in early 2020, a "public health emergency," triggering significant problems to the Chinese economy.The pandemic has had a massive effect on the majority of industries.This article will apply the Event Analysis Approach based on Granger Causality Test to examine the epidemic's influence on the stock returns of public companies in China's pharmaceutical industry by using the Shenwan Pharmaceutical Biological Index as an example.The Granger causality test is a statistical hypothesis test used to determine if one time series can be used to predict another.If the probability value is less than any α level, then the hypothesis would be rejected at that level.The research findings reveal that the COVID-19 pandemic has a substantial positive impact on the profitability of China's pharmaceutical sector, although the influence is only temporary.The importance of studying the impact of epidemics on capital markets is that the findings can provide a theoretical basis and practical recommendations for regulators and investors to make decisions during emergencies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".