Securities Settlements as Examples of Crisis-Driven Regulation
Bibliographic record
Abstract
International bodies have criticized Canadian financial markets for being lax in the area of enforcement. We examine whether such criticisms are applicable to settlements struck by the Ontario Securities Commission (OSC). We reach a number of important findings. First, the total number of parties sanctioned and the total amount of payments made to the OSC increased after the 2008 financial crisis, although these numbers decreased in subsequent years. Second, there is no discernible trend in the types of proceedings by which cases were concluded, although the OSC does use settlements more than other provincial regulators. Third, corporations, first-time offenders, and financial service companies are more likely than individuals or repeat offenders to settle and the OSC tends to settle less often when the case involves serious offences such as fraud or manipulation. Finally, penalties imposed as a result of a settlement were not statistically different than those imposed in a hearing. Interestingly, while there are outliers, financial service companies did not pay higher penalties than other parties, nor did repeat offenders although this has recently changed with the introduction of no-contest settlements. Our data support the idea that regulatory activity follows a cyclical pattern and, following a crisis, regulatory activity increases.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".