Coronavirus (Covid-19) and Stock Market: Empirical Analysis with Panel Data Approach
Bibliographic record
Abstract
In this study, the relationship between the pandemic and the stock market range of the dates 17-03-2020 and 14-04-2020, when the COVID-19 pandemic was most intense, was examined by panel data analysis method. In this study conducted for Turkey and Belgium, Germany, France, Italy, Spain, United Kingdom, United States, China and Netherland countries where the COVID-19 pandemic is most common, COVID-19 data is based on the total number of cases and the total number of deaths, while stock market data is based on important stock indexes of countries. The results of the study, while a negative relationship was found between total number of cases and the stock market, a positive relationship was found between total number of death and the stock market. This is an indication that market investors are closely monitoring the number of COVID-19 cases, and that the number of cases described significantly affects stock market investments.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".