Early Warning Signals From Global Financial Markets at the Beginning of Covid -19 Pandemic
Bibliographic record
Abstract
The Covid-19 Pandemic pose health security risks all over the world and makes its effects felt in every field. The objective of this paper is to observe the response of the financial markets of countries with the highest global health security index (GHSI) when the pandemic cases started and to guide investors. The main areas of the index are prevention, detection, reporting, rapid response, health system, compliance with international norms and risk environment. Many indicators are monitored under these basic areas. The motivation point of the study is that comparing the financial markets of countries with the highest global health security index has never been investigated. The top 5 countries selected are as follows: The United States, The United Kingdom, The Netherlands, Australia and Canada. In the selection of the stock exchange, the most known indexes of the countries on a global scale and the highest trading volume are taken. The common transaction days are taken as a basis short period that starts on 28 February 2020 and ends on 15 May 2020. This study showing early warning signals from global financial markets will also be a guide for future studies and long-term analysis. Dumitrescu Hurlin Panel Causality Test applied to the data; the causality from death and case rates to stock market returns is investigated. Hidden causality is important in decision making. Investors should make international investments by thinking in detail about the assets in their portfolio. In the near future, the importance of hidden causality relations research and early warning signals that occur with the effect of financial contagion will increase.
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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 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".