Flash Boys Class Actions: Civil Fraud, Conspiracy, and the Certifiability of High-Frequency Trading Cases in Canada
Bibliographic record
Abstract
ABSTRACT: The global COVID-19 pandemic has caused significant volatility in global stock markets, reigniting concerns around the regulation of high-frequency traders. High-frequency traders are electronic traders who use algorithms to execute hundreds of trades in the time it takes to blink an eye. Although some of these traders are harmless, others use their speed advantage to prey on ordinary investors, generating over $5 billion globally in increased trading costs. This article proposes that class action law could protect investors from predatory high-frequency trading behaviours where regulation falls short. Part A provides an overview of high-frequency trading, discussing both its purported market benefits as well as its harmful effects on investors and capital market volatility. Part B discusses the problem of regulators lagging behind in the complex high-frequency trading industry. It then contends that class actions can supplement deficient enforcement efforts, increase investor confidence in the market, compensate harmed investors, and force high-frequency traders to internalize the costs of their behaviour. Part C discusses two high-profile high-frequency trading class actions in the United States. It then analyzes the doctrinal differences between Canadian and US securities law, and applications to the certification process. Part D concludes by suggesting that high- frequency trading class actions are ultimately a viable, although challenging, solution. This article, therefore, proposes modest reforms to securities law — such as modernizing the statutory offences of fraud and market manipulation and creating private rights of action for such offences — to increase the availability of class actions and enhance access to justice for investors in modern capital markets.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".