Do COVID-19 cases and government response to COVID-19 drive mispricing in cross-listed companies?
Bibliographic record
Abstract
This research is to examine�impact of COVID-19 cases and government response to COVID-19 to cross-listed companies stock return, mispricing and volatility of the mispricing, using evidence from 164 Canadian companies stocks listed on Toronto Stock Exchange (TSX shares) and cross-listed in New York Stock Exchange or NASDAQ (US shares). Our results show that there is negative impact from growth of COVID-19 cases to stock return�and positive impact from government response to COVID-19 to stock return in both Canada and US. As for impact to mispricing, using price premium of stock listed in Canada relative to US as a proxy,�the results suggest that�when we implemented separate variables of growth of COVID-19 cases and government response to COVID-19 for each country, only government response to COVID-19 in Canada was found to have significant positive impact to the price premium. We have further examined the impact by implemented variables of difference in COVID-19 cases growth and government response to COVID-19 between Canada and US instead of separate variables for each country, the results showed that difference in COVID-19 cases growth of Canada relative to US is significantly negatively related to the price premium while difference in government response to COVID-19 cases is significantly positively related to the price premium. As for impact to volatility of the price premium, COVID-19 cases growth in US and government response in both Canada and US are found to be positively related to volatility of the price premium.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".