Bibliographic record
Abstract
The COVID-19 shock is severe and its severity is even more when compared to the Great Financial Crisis. Yet, the effect of the COVID-19 on the stock markets and financial markets has never been studied in depth in recent times. This research is carried out to study the impact of the global pandemic Corona Virus on the Financial Markets from 1st March 2020 to 30th April 2020 in G7 countries. The study applied a Simple regression and Correlation model to investigate the impact of the COVID-19 on the Financial Markets during the period 1st March 2020 to 30th April 2020 in G7 countries. The study used the Cotation Assistee en Continu (CAC) index for France, Deutscher Aktienindex (DAX) index for Germany, Milano Indice di Borsa (FTSE MIB) index for Italy, NIKKEI index for Japan, Dow Jones index (DJI NYSE) for USA, FTSE 100 for UK and TSX index for Canada. In the process of studying the impact of Corona Virus on the stock markets the study assumes the confirmed cases of COVID-19 to be the independent variable while CAC index, DAX index, FTSE MIB index, DJI NYSE index, FTSE 100 and TSX index to be dependent variables. The study findings revealed that there is a positive significant relationship between the COVID- 9 confirmed cases and all the financial markets from 1st March 2020 to 30th April 2020 in G7 countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".