The extent and intensity of insider trading enforcement – an international comparison
Bibliographic record
Abstract
This article presents the results of a detailed comparative empirical study of sanctions imposed for insider trading in Australia, Canada (Ontario), Hong Kong, Singapore, the United Kingdom, and the United States. The comparative study is based on a dataset of a significant size, scope and comprehensiveness, encompassing nearly 700 individuals and companies, as well as approximately 1400 sanctions imposed for the contravention of insider trading provisions during the seven year period from 1 January 2009 to 31 December 2015. The study compares the type, magnitude and frequency of sanctions imposed by statutory bodies and the courts for insider trading and provides important insights into the enforcement tools commonly used by securities regulators to enforce insider trading laws. One significant finding is that even in jurisdictions with similar insider trading laws, very different sanctions are used to enforce these laws. The article also sets out an empirical methodology for assessing the severity of sanctions imposed for insider trading in each of the jurisdictions, providing an example for future empirical analysis.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".