Why Do Canadian Firms Cross‐list? The Flip Side of the Issue
Bibliographic record
Abstract
We investigate the relation between managerial incentives and the decision to cross‐list by comparing Canadian firms cross‐listed on US stock exchanges to industry‐ and size‐matched control firms. After controlling for firm and ownership structure characteristics, we find a positive association between substantial holdings of vested options held by CEOs prior to cross‐listing and the decision to cross‐list. Further, firms managed by CEOs with substantial holdings of vested options exhibit positive announcement returns and negative post‐announcement long‐run returns. CEOs of cross‐listed firms seem to take advantage of the aforementioned market behaviour, because they abnormally exercise vested options and sell the proceeds during the year of listing only when their firms underperform during the subsequent year. In addition, there is a positive relation between substantial holdings of vested options and discretionary accruals during the year of listing, consistent with the view that CEOs manage earnings to keep stock prices at high levels. Overall, these results have significant implications for the cross‐listing literature, suggesting an association between cross‐listing and CEO incentives to maximize CEO private benefits.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".