COVID-19: A Game-changer to Equity Markets?
Bibliographic record
Abstract
This article applies the effective transfer entropy methodology to quantify the information flow between equities in major global equity markets in Australia, Brazil, Canada, China, Germany, Iran, Japan, Qatar, Saudi Arabia, South Africa, South Korea, United Kingdom, and the United States – a pool of 2200 companies included. To account for COVID-19 impacts, the period of the study was extended over two years. The results show changes to the information flow pattern after COVID-19, with the largest changes being encountered in Australia, Brazil, Canada, Japan, and the United States – for their largest market participants (by market capitalization). In comparison, the Asian markets show less noticeable changes in their information flow pattern after COVID-19. On a sector level, most of the markets studied have seen substantial changes in the functionality of their sectors – in terms of being a transmitter or receiver of information – after COVID-19 appearance. The fraction of sectors with a complete change in their influencing role since COVID-19 has been over 70% in Australia, Canada, South Africa, and the United States. The financial services sector has retained its role - as being the most influencing sector - in 6 out of 13 markets considered after COVID-19. For most of the markets, the basic materials, communications, energy, and utilities sectors have retained an intermediate position in the information flow diagram, after COVID-19. The German market has been the only market, in which the main information transmitter and receiver sectors have remained unchanged, since COVID-19. The results suggest drastic moves in major global equity markets, which have been concurrent with the virus outbreak. Doi: 10.28991/HEF-2021-02-01-05 Full Text: PDF
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".