PROTECTING MARKET INTEGRITY IN AN ERA OF FRAGMENTATION AND CROSS BORDER TRADING
Bibliographic record
Abstract
Stock exchanges and trading on them has changed dramatically in the last few decades as markets for securities have fragmented, trading volumes have escalated and the opportunities to trade in different markets and across international borders has increased. These changes to the markets have been driven principally by a focus on improving market efficiency, liquidity and investor choice rather than protecting the integrity (or fairness) of the markets. Yet some of these changes may have had an adverse impact on market integrity and, in particular, may have increased the ability of market participants to engage in market abuse such as insider trading and market manipulation. In response to these changes, securities regulators have endeavoured to adapt to this new trading environment, but has the reaction of regulators been satisfactory to protect the fairness of markets? This article seeks to explore this question by outlining the changes, considering how they may have impacted upon market integrity and analysing the regulatory response. Finally this article argues that to successfully maintain and improve market integrity considerably more needs to be done to improve the collection, exchange and analysis of information to maintain effective market oversight.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".