Securities Regulation Reform and the Decline of Rights Offerings
Bibliographic record
Abstract
Abstract This paper develops the hypothesis that the decline in the use of rights offerings is due to reductions in issue costs brought about by changes in securities regulation. The hypothesis is tested in two jurisdictions: Canada and the United States. Time series analysis is used to determine if the decreased use of rights offerings in the 1970–1985 period is associated with regulatory changes designed to ease stock issues, such as short form registration and shelf registration in the U.S. and the Prompt Offer Qualification (POP) system in Canada. The findings are consistent with a significant decrease in rights usage concurrent with the earliest reform in each country. Résumé La présente étude avance l'hypothèse que la réduction de l'emploi des droits de souscription est due à la diminution des coûts d'émission permise par les réformes des lois sur les valeurs mobilières. L'hypothèse est testée dans deux juridictions: le Canada et les États‐Unis. Nous faisons une analyse de données chronologiques pour déterminer si la réduction du nombre de droits de souscription entre 1970–1985 reflète les réformes des règlements conçues pour diminuer les coûts d'émission, par exemple, aux États‐Unis, l'enregistrement simplifié et l'enregistrement préalable et, au Canada, le Régime du prospectus simplifié. Les résultats sont compatibles avec une diminution significative de l'emploi des droits de souscription, dans chaque pays, dès les premières réformes.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".