An update on self-regulation in the Canadian securities industry (2009-2016)
Bibliographic record
Abstract
Purpose This paper aims to analyze the processing of complaints against investment advisors and Member firms through the Investment Industry Regulatory Organization of Canada (IIROC) enforcement system between 2009 and 2016. The paper used the misconduct funnel to show the number of complaints that are “funneled in,” and how these complaints are subsequently “funneled out” and “funneled away” at the investigation and prosecution stages of IIROC enforcement system. Design/methodology/approach The paper uses data from IIROC enforcement annual reports from 2009 to 2016. A combination of descriptive statistics and correlation matrices was used to analyze the data. Findings The findings indicate that while IIROC “funneled in” more complaints, a significant proportion of complaints were “funneled out” of its enforcement system and funneled “away” from the criminal justice system. Fines imposed were often not collected from individual offenders. IIROC, it seems, is ineffective in handling the more serious and systematic industry problems. Practical implications It is hard not to see the findings from this study being used by the provincial securities commissions and the federal government to support the call for a national securities regulator in Canada. Originality/value This is the first study of its kind to systematically analyze the enforcement performance of IIROC.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.038 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".