Recent regulatory developments provide some clarifications regarding the market access rule for broker-dealers
Bibliographic record
Abstract
Purpose – To provide an overview of recent developments relating to the Securities and Exchange Commission (SEC)’s Market Access Rule, Rule 15c3-5 promulgated under the Securities Exchange Act of 1934. Design/methodology/approach – Provides a brief overview of the Rule’s requirements; highlights key points of guidance from the Frequently Asked Questions released by the Staff of the SEC’s Division of Trading and Markets in April 2014; and discusses the SEC’s first enforcement actions for alleged violations of the Rule, which include a settlement with Knight Capital Americas, LLC and administrative and cease-and-desist proceedings instituted against Wedbush Securities, Inc. Findings – The SEC has prioritized its focus on Rule 15c3-5, which has resulted in the issuance of FAQs and enforcement actions against broker-dealers for violations of the Rule. While the FAQs and the Knight Capital settlement provide some insight into the Enforcement Staff’s view of what the Rule requires, there are still areas where the substantive requirements are not entirely clear. Originality/value – Practical guidance from experienced securities lawyers that consolidates several recent developments in one piece.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.017 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".