Insider trading in China: the case for the Chinese Securities Regulatory Commission
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the effectiveness of illegal insider trading enforcement in China by focusing, among other things, on the Chinese Securities Regulatory Commission's (CSRC) enforcement actions in the period 1993‐2006. Design/methodology/approach This paper discusses the CSRC's enforcement policies and practices of insider trading regulation, based upon administrative and judicial cases, face‐to‐face interviews with regulators, and policy documents. Findings A major finding of the study is the paucity of insider trading cases and the lack of convictions for insider trading offences in China. The campaign against securities offences did not actually come with the stricter enforcement of insider trading laws. A primary challenge in the insider trading regulation comes from the fact that most insider trading cases involve high‐ranking government and party officials. The CSRC lacks the power to directly administer discipline and penalties on government officials and party cadres for insider trading offences. Research limitations/implications It is recommended that the CSRC be given more power, more resources and more trained regulators to detect and address insider trading activities. It is also recommended that the CSRC improve its surveillance capabilities by fully utilizing sophisticated computer surveillance software systems, by improving inter‐agency and inter‐market information‐sharing, and by cooperating with other countries' regulators and participating in the ISG's database to detect possible international insider trading. Originality/value The paper will be of interest to researchers in the field of financial crime and securities regulation. Regulators, the private sector and government departments will also benefit from an analysis of Chinese insider trading enforcement cases. This paper also suggests better strategies for dealing with insider trading offences in China. A fair and orderly market is crucial for investors in the Chinese market.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".