IMPACT OF PANDEMIC COVID-19’S ON NATIONAL CURRENCY AND FINANCIAL MARKETS: AN ANALYSIS ON DEVELOPING AND DEVELOPED COUNTRIES
Bibliographic record
Abstract
Coronavirus outbreak which started as an epidemic in Wuhan, China and soon transformed into a pandemic in the first quarter of 2020 is about to bring a profound stagnation to many national economies. In the study, to understand the effects of the covid-19 pandemic on national currency and financial markets from the perspective of developing and developing countries, daily data including the stock market closing prices, exchange rate and WTI gross oil prices of the effects of COVID-19 in the period of March 10, 2020 and May 9, 2020 for developing and developed economies were used. China, South Korea, Brazil, and Turkey are chosen to represent the developing world and Italy, France, Germany, Spain and England represent the developed world. Logarithms of all variables were taken and in the econometric application part, vector autoregression model was used. At the end of the study, it was determined that the number of Covid-19 cases did not affect exchange rates, but had an effect on stock prices in developing economies. As a result, It has been determined that developing economies affect more than developed economies from pandemic.
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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 | 0.001 |
| 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 | 0.000 | 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".