Price Discovery in the Cross Listed Stock Market: Revisiting the Case of Canadian Stocks Listed in the United States
Bibliographic record
Abstract
This paper revisits studies conducted by Rosenthal and Young (1990) and Froot and Dabora (1999) that found prices of twin stocks to be mispriced and that this mispricing could be explained by the markets in which the shares are listed. Our study investigates whether these findings can be generalized to Canadian firms who cross-list in the US. Using a sample of 184 firms who cross-listed during the period 1975 – 2013, we also observe share mispricing that can be explained by the markets in which the shares are listed in, however it is not trading activity alone that determines the significance of this relationship. Furthermore, we observe a discrepancy in the co-movement of Canadian-listed shares and their US-listed counterparts with currency fluctuations, making this the most significant factor in explaining the mispricing observed in our sample of cross-listed firms.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".