Measuring Financial Impact Of COVID-19 Pandemic On Global Stock Markets -
Bibliographic record
Abstract
The present study has analyzed the impact of COVID-19 on the stock market. The study was intended in the context of Pakistan Stock Exchange. The paper attempted on the basis of two major objective i.e. analyzed direct impact of COVID-19 on the stock market and the COVID-19 impact on foreign markets spillover effects on Pakistan Stock Exchange. The paper has adopted conventional t-test and Mann Whitney test on the average return on the basis of stock market indexes. The results have been estimated on two basis i.e. domestic & foreign timeline. Three event windows were selected for the estimation of average return i.e. pre-event window (starting time of COVID-19), short-event window (increasing time of COVID-19 cases) and long-event window (Peak time of COVID-19). The time period has been taken from January 1st to 30th December 2020. The stock markets of Pakistan, India, Canada, Japan, UK, & United States were included in the study. The findings show (1) COVID-19 has negative but limited impact on the stock market; (2) there is an evidence of spillover effect of COVID-19 on Europe and US markets on Pakistan Stock Exchange. The findings contribute towards the economic and financial impact of COVID-19 in the context of stock market. The proof has been received that the Europe and US markets have impact on the Asian Stock Markets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".