Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze and compare the influence of the Sarbanes–Oxley (SOX) Act of the USA and the Canadian SOX (C-SOX) through the comparison of corporate acquisitions in these two countries. Design/methodology/approach The final sample includes 1,187 merger and tender offers undertaken by publicly traded (TSX listed) Canadian firms between 1990 and 2016. The authors use standard event study methodology (Patell, 1976) and Berkovitch and Narayanan’s (1993) seminal method to examine announcement period performance and deal motive, respectively. Findings The findings support the pro-regulation hypothesis which states that stricter regulations are more useful. Cross-listed acquirers exposed to SOX regime performed much better (both short- and long-term) than non-cross-listed counterparts with only C-SOX exposure. These findings are both statistically and economically significant. Research limitations/implications This study has direct implications as it provides evidence to the legislatures of the provinces, as well as to the federal government, that stricter regulations are effective and Canada should enact additional corporate legislation. Canada may have fared well in the past, but dynamics are changing and may further change in the future, and therefore, timely and stricter corporate legislation are more appropriate. Practical implications This study has direct implications as it provides evidence to the legislatures of the provinces, as well as to the federal government, that stricter regulations are effective and Canada should enact additional corporate legislation. Canada may have fared well in the past, but dynamics are changing and may further change in the future, and therefore, timely and stricter corporate legislation are more appropriate. Originality/value This study contributes to the growing literature of SOX-related studies. This is the first study to investigate comprehensively the differences between the two laws enacted by these neighboring countries. As USA and Canada share largely integrated capital markets and are each other’s biggest trading partner, this genre of research has great value. It is a timely study as the Canadian Federal Government is looking into standardizing corporate legislation across provinces and territories.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".