Unusual Trade or Market Manipulation? How Market Abuse Is Detected by Securities Regulators, Trading Venues and Self-Regulatory Organizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Securities regulators protect the integrity of capital markets by detecting, investigating and then prosecuting, insider trading and market manipulation, known collectively as market abuse. The main method of detection of market abuse is surveillance of markets by software designed to identify unusual trading. Such market surveillance is undertaken by trading venues, self-regulatory organizations, and securities regulators. Two other important methods used to detect market abuse are suspicious transaction reports made to regulators by financial intermediaries and voluntary reports to regulators by the public of possible market abuse.This article describes the main methods of detection of market abuse in five jurisdictions which comprise over 50 per cent of the world’s securities markets, namely the USA, Canada, Germany, the UK, and Australia. Furthermore, given the growing internationalization of securities markets, there exists the possibility that many market abuse offences will not be confined to one country and may span two or more jurisdictions. As such regulatory bodies must increasingly work together to exchange information to detect market abuse. This article also examines how such information is exchanged, the challenges in exchanging such information and suggests ways in which this could be improved. In particular, more investment is needed in market surveillance systems to improve both the detection of market abuse and to enable securities regulators to swiftly determine the cause of any market disruption. This will in turn facilitate a more targeted regulatory response to ensure that such a disruption is not repeated.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it