The Covid-19 Pandemic and the Consumer Staples Sector: A Test of Market Efficiency
Bibliographic record
Abstract
On Wednesday March 11, 2020, the World Health Organization (WHO) declared the covid 19 outbreak a global pandemic (Cucinotta, 2020). How efficient is the consumer staples market reaction to the announcement of a global pandemic? The purpose of this study is to examine the risk adjusted returns on and around the pandemic announcement to test the semi-strong form market efficiency hypothesis using the standard event study methodology in the finance literature. Will returns in the consumer staples industry show larger than expected gains on and surrounding the WHO’s COVID-19 pandemic announcement? The finance literature offers little evidence supporting the link between pandemics and the stock market. The announcement of a pandemic by the WHO should significantly and quickly affect the market. According to Eugene Fama (1970), if the market is semi-strong form efficient, all public information is immediately factored into the market and no investor can use this information to achieve an above normal return when adjusted for risk. To study this relationship, data for the S&P 500 and 10 consumer staples sector firms were collected for the event period surrounding the announcement of the COVID-19 pandemic. This study tests the global pandemic announcement for semi-strong form market efficiency (Bacon &Howell 2021). Evidence here supports the expected positive signal associated with the sample of consumer staples sector firms in reaction to the announcement of the pandemic. Likewise, the study results support the semi-strong form efficient market hypothesis and suggest the possibility of trading on this information up to 15 days prior to the announcement consistent with the behavioral finance literature (Bacon & Howell 2021).
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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 | 0.005 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".