The Impact of Coronavirus Pandemic on Stock Market Return: The Case of the MENA Region
Bibliographic record
Abstract
This paper attempts to investigate the impact of Coronavirus spread on the stock markets of MENA region. Coronavirus has been measured by cumulative total cases, cumulative total deaths, new cases and new deaths, while stock market return is measured by Δ in the stock market index. This has been applied on stock markets of 7 countries (Egypt, Jordan, Morocco, Qatar, Saudi Arabia, United Arab Emirates, and Tunisia), on daily basis during the period from March 1, 2020, to July 24, 2020. Results indicate that stock market returns in the MENA countries tend to be negatively affected Coronavirus cumulative deaths and Coronavirus new deaths. A robustness check has been conducted for each country during the whole period, showing significant effect of Coronavirus cumulative cases in Jordan and Tunisia and significant effect of Coronavirus cumulative deaths in Jordan, Morocco and Tunisia, without any evidence about the effects of Coronavirus new cases and Coronavirus new cases. After splitting the research period into 4 sub-periods (March, April, May, June- July 24), results support the impact of “cumulative Coronavirus cases” on stock market return in Jordan during May and in Morocco during April. Besides, the impact of “cumulative Coronavirus deaths” has been supported in in Morocco during April, and in Tunisia during March and June-July. Moreover, “new Coronavirus cases” seems to have a significant impact in Jordan during May and in Tunisia during March. Also, “new Coronavirus deaths” shows a significant effect in Morocco during May.
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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) | 0.001 | 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".