The Early Impact of Government Financial Intervention Policies and Cultural Secrecy on Stock Market Returns During the COVID-19 Pandemic: Evidence From Developing Countries
Bibliographic record
Abstract
This paper examines the role of government financial intervention policies and cultural secrecy on equity market returns during the start of the COVID-19 pandemic in developing countries’ stock markets. We employ global data including 939 observations across 32 developing countries (23 emerging and 9 frontier stock markets) from December 1 to April 28, 2020. Our results show that the above-mentioned policies that set out to curb the COVID-19 pandemic succeed in increasing equity returns. It reflects investors’ improved perceptions of governments’ commitment to stabilizing the economy during the pandemic in developing, emerging, and frontier equity markets. Results show that investors in all equity markets discount differences in cultural secrecy in processing market information when investing in stock markets. We find that equity market investors in developing and emerging countries truly react negatively to the rise in the number of confirmed COVID-19 cases reported. Yet, we find that COVID-19 wields no influence on equity market returns in frontier equity markets. This presents frontier equity markets as a safe-haven investment destination during a global health outbreak. Our work helps investors during such events to identify the best and worst investment destinations in developing, emerging, and frontier stock markets. At the same time, it is important to understand the critical roles of: firstly, the introduced government financial intervention policies; and secondly, the daily growth in reported COVID-19 cases on stock market returns.
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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 | 0.009 |
| 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 | 0.001 |
| 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".