COVID-19 as Information Transmitter to Global Equity Markets: Evidence from CEEMDAN-Based Transfer Entropy Approach
Bibliographic record
Abstract
This study provides an analysis of chaotic information transmission from the COVID-19 pandemic to global equity markets in a novel denoised frequency domain entropy framework. The current length of the pandemic data offers the opportunity to examine its role in the asymmetric behaviour patterns of investors according to time horizons and the diversification potentials available to them. We employ the total daily global confirmed cases of COVID-19 and 27 equity indices from December 31, 2019, to April 18, 2021. Our results corroborate the idea that diversification potentials are stronger in the short to medium term. The Global Index (higher risk) and Canada and New Zealand (lower risk) remain at both ends to pair some other equities to offer diversification prospects because of the transmission of information from COVID-19 to the selected equity markets. In addition, we provide the source of these diversification prospects as information flow rather than transmission of shocks, which is common in the literature. Furthermore, our results suggest detailed levels of risk (lower vis-à-vis higher) in the situation where they have been stripped of the noise in the market. The findings allow both investors and policymakers to make informed decisions based on the time horizons since the pandemic communicates different chaotic information with the lapse of time. This is imperative to avoid the negative consequences of the increasing infection rate on global stock markets.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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".