Quote dynamics of dually-listed stocks
Bibliographic record
Abstract
This study investigates the quote dynamics of stocks listed and traded in two international fullysynchronized \nmarkets. We develop a general model for quote dynamics of assets traded in dual \nmarkets to assess how quotes react to liquidity shocks and trade-related information. We further \ndevelop this model to extract the implied vector autoregression for the spreads, the e¢ cient price, \nand the relative premium between the two markets. Applying our model to a sample of 64 Canadian \nstocks listed both in the U.S. and Canada, we observe a strong evidence of cross-market errorcorrecting \nbehavior of spreads on the bid and ask quotes, indicating some degree of intermarket \ncompetition between liquidity providers. We also Önd that trade-related information does not \na§ect quotes across market directly, indicating that even though the prices in the two markets are \ncointegrated, the two markets are still informationally segmented. Microstructure fundamentals \nsuch as changes in midpoint (implied e¢ cient price) and the di§erence in midquotes (relative \npremium) are driven by liquidity and trade-related information from each of the two markets with \nthe U.S contributing more than Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.009 |
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; both teacher heads agree on what is shown here.
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".