The 'Fraud-on-the-Market' Theory for Securities Litigation Arrives in Canada: Two Steps Forward and One Back?
Bibliographic record
Abstract
The Fraud-on-the-Market theory holds that high volume markets, such as the New York or Toronto stock exchanges, effectively incorporate all available information of present and expected value into a security's price. As endorsed by the United States Supreme Court, and reflected in a recent amendment to the Ontario Securities Act, fraud on the market serves an important procedural role within the context of securities litigation by providing a general reliance presumption for all investors. For when a market is assumed to be efficient, misinformation is deemed to defraud investors who have relied upon the integrity of the market itself. Although efficiency has been advanced in this recent instance of legal emulation, by encouraging market transparency without the costs and risks of original legislative design, it also has incorporated the problem of over compensation for plaintiffs inherent to the copied original. For the soundness of market information to be encouraged through a reliance presumption, the effect should not become counter-distorting through the double compensation of litigation loss. If fraud on the market is to be applied with consistent logic, then one must assume that the instant a misrepresentation is made public that rational investors will automatically discount the stock price to account for the inevitable class action suit that will arise. Though a subsequent, post misrepresentation fall in share price has traditionally been the basis of loss claims (as confirmed in the Ontario Act), a class of investors should not be able to recover on an amount that includes the market's anticipated cost of their future litigation. The following paper draws upon methods of alternative company valuation to arrive at a simple formula for filtering out anticipated litigation loss from securities damages.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.028 | 0.026 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.023 | 0.025 |
| Insufficient payload (model declined to judge) | 0.012 | 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".