Legal Protection of Investors from the Corporate Malfeasance of Insider Dealings: A South African-Canadian Comparative Review
Bibliographic record
Abstract
Ensuring market discipline, integrity, and transparency with the overall aim of protecting the investing public is critical to the wellness of a capital market and a financial system. However, one corporate ill besetting the securities markets in all jurisdictions is insider trading. Apart from being unethical, insider trading disrupts market dynamics. In South Africa, over the years, successive Acts have been enacted, amended, and repealed to ensure discipline and protect the integrity of the nation’s securities market. In 2012, the Financial Markets Act of 2012 (FMA) was enacted to improve, among others, the enforcement of insider trading regulation in South Africa. However, the regulation of insider trading and its enforcement in terms of the FMA have been insufficient. This article therefore seeks to benchmark the South African position against Canadian model with the objective of drawing lessons for South Africa. The choice of Canada was informed by the fact that Canada has a well-developed anti-insider trading regulatory framework and presents a case study of international best practices in the regulation of insider trading. Therefore, the conclusion in this article is that with creative and appropriate reforms of the FMA, using the Canadian model, the investing public will be adequately protected against insider trading, and investors’ confidence and the financial markets’ integrity and efficiency will be better enhanced.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".