DETERMINANTS OF UNDERWRITINGFEES FOR DOMESTIC AND NON-DOMESTICSEASONED EQUITY OFFERINGS BY CANADIANCROSS-LISTED SHARES
Bibliographic record
Abstract
This paper examines whether the determinants of underwriter fees are the same for domestic and non-domestic seasoned equity offerings (SEOs) by Canadian shares cross-listed on the TSE and on the NYSE/AMEX and NASDAQ. The results indicate that gross proceeds, firm size, return volatility, relative size of the offering and the inclusion of an overallotment option are the determinants of fees for domestic SEOs. Firm size, number of underwriters, type of offering and U.S. listing venue are the determinants of underwriting fees for non-domestic SEOs. After controlling for differences in other relevant fee determinants, underwriter fees are significantly higher for non-domestic compared to domestic SEOs, and for non-domestic SEOs for Canadian shares cross-listed on the NASDAQ compared to those cross-listed on the NYSE/AMEX. These results suggest that the Canadian and the U.S. investment banking markets are not integrated in the sense of sharing underwriting cost functions with an identical set of determinants.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".