Stock market performance, COVID-19 related government measures, and immunization: Evidence from the G7
Bibliographic record
Abstract
Research Question:What impact has the COVID-19 transmission rate, death rate, state involvement, and vaccine growth rate had on the stock market returns of the group of seven countries?Motivation: On the one hand, the majority of the literature has only looked at the impact of government interventions, the number of confirmed Covid 19 cases, and the number of deaths on stock markets individually, not their combined impact.Additionally, there hasn't been any research done in the literature up to now on the effect of vaccination on stock market returns.By examining the impact of all four factors-vaccination growth rate, mortality rate, and speed of transmission of covid 19-on the stock market returns of the group of seven G7 countries, our study aims to close this difference.This study is the first comprehensive attempt to investigate the relationships among governmental engagement, COVID-19 vaccination, and stock market returns.Idea: This study explains how stock markets in the G7 countries reacted to the spread rate of Covid 19, mortality rates, containment measures, and immunization Data: The data was collected from a number of sources.The www.investing.comwebsite provides information on daily stock market performance for the G7 economies.S&P 500 (US), FTSE 100 (UK), TSX (Canada), DAX 30 (Germany), CAC 40 (France), MIB (Italy), and Nikkei 225 (Japan) are the only major stock indices we choose.The www.ourworldindata.orgwebsite is used to collect the data required to calculate our covid 19 variables (Transmission speed, Mortality rate, Growth in immunization, Containment, and Health Index).
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".