Enforcement of Chinese Insider Trading Law: An Empirical and Comparative Perspective
Bibliographic record
Abstract
Abstract This Article conducts the first comprehensive and systematic empirical analysis of all relevant insider trading cases in China from the birth of Chinese securities markets in the early 1990s until mid-2017, shedding light on the way in which China’s insider trading law has been enforced by the regulator and criminal courts in practice. First, the Article generates descriptive statistics on features of insider trading cases, such as the total number of cases over the study period, the temporal distribution of the cases, the identity of the insider, and the nature of the insider information. Second, it measures the intensity of insider trading enforcement and compares the Chinese situation with six overseas jurisdictions, including the United States, the United Kingdom, Australia, Canada, Singapore, and Hong Kong. Third, using multiple regression analyses, it identifies potential factors determining the administrative and criminal penalties for insider trading. The results of the empirical study indicate that China has significantly stepped up its efforts to crack down on insider trading in recent years, resulting in a sharp increase in insider trading cases, particularly criminal cases since 2008. While the Chinese insider trading law was essentially transplanted from overseas jurisdictions, its; enforcement has exhibited distinctive features in its local environment. Judging by the type, magnitude, and frequency of the sanctions imposed, the intensity of insider trading enforcement in China seems to be at a level comparable to relevant jurisdictions overseas. Administrative and criminal penalties against insider trading are found to be significantly influenced by some factors, notably the amount of illegal proceeds, the magnitude of social impact, the presence of mitigating circumstances, and whether the trader used others’ accounts to trade. The hope is that the empirical findings will help inform the policy debate over the regulation of insider trading in China and beyond.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".