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 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 | 0.001 | 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 | 0.000 |
| 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".