Self-regulation and compliance enforcement practices by the Investment Dealers Association in Canada
Bibliographic record
Abstract
Purpose This paper aims to examine the enforcement practices of the Investment Dealers Association of Canada (IDA) and argue that self-regulation simply does not work in the financial sector, as the sanctions available are neither applied with sufficient severity nor are the responsibilities for enforcement adequately divided between self-regulation, provincial securities commissions and the police. Design/methodology/approach The core compliance data for the study came from the IDA’s tribunal cases that were heard between 1984 and June 2008. The theoretical approach involves the invocation of classic articles by the likes of Stigler, Posner and Becker, the essence of whose conclusions is that institutions will act in their own best interests and cannot be expected to act in the public interest. Findings The findings show that over the period from 1984 to 2008, the severity of the sanctions increased consistently over the period. When penalty ceilings were increased, penalties increased. When in the latter phase of the period, public members (i.e. non-members of the industry) chaired the tribunals, penalties also increased. Research limitations/implications Researchers can use the data to write a paper which asks “Why did the IDA tribunal penalties increase so consistently with time?” Future research could canvass various possible explanations, including the one presented in this paper, to focus sustained attention on the issue of self-regulation. Originality/value This study is the first to systematically examine the enforcement performance of the IDA.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".