Private Regulation of Insider Trading in the Shadow of Lax Public Enforcement (and a Strong Neighbor)
Bibliographic record
Abstract
Like U.S. firms, many Canadian firms voluntarily restrict trading by corporate insiders beyond the requirements of insider trading laws (i.e., super-compliance). Thus, we aim to understand the determinants of firms' private insider trading policies (ITPs), which are quasi-contractual devices. Based on the assumption that firms that face greater costs from insider trading (or greater benefits from restricting insider trading) ought to be more inclined than other firms to adopt more stringent ITPs, we develop several testable hypotheses. We test our hypotheses using data from a sample of firms included in the Toronto Stock Exchange/Standard and Poor's (TSX/S&P) Index. Our empirical results suggest that Canadian firms do not randomly restrict insider trading, but rather do so predictably and with a predictable level of intensity, suggesting that some firms wish to control insider trading to enhance corporate performance. Our most robust finding is that firms with a greater prevalence of controlling shareholders are more likely to have adopted a super-compliant ITP than firms with fewer such shareholders, implying that influential shareholders may oppose insider trading and challenging the claim that private restrictions of insider trading would not arise in the absence of insider trading laws.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".