Bibliographic record
Abstract
Enforcing regulators worldwide are mandated to discourage the use of privileged undisclosed information in security trading, as it may distort the true relative prices of securities and may lead to a market failure.This may be an impossible task, given the huge number of indications of such suspicious activity.Deviations of the bid-ask spreads from the optimal spreads provide real-time indications that some market participants may possess undisclosed privileged information.If we relied only on the real-time information that the bid-ask spreads deviate from the theoretical optimal spreads, our 3-years sample would generate over 11M deviations.This quantity of indications is unmanageable, both for the enforcing regulator, and for the uninformed market participants.In this study, we construct a mechanism that will enable market participants to significantly reduce the number of suspected cases to a manageable number.We establish an ex-post calibrated likelihood index that estimates the likelihood that profit-oriented traders desire to exploit undisclosed privileged information that they possess.Our mechanism is based on the hypothesis that at any point in time when the likelihood index that predicts how likely traders are to exploit privileged information is positively correlated with an abnormal increase in the bid-ask spreads, then, probabilistically, these transactions should be suspected cases of illegal price-distorting trading.
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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 | 0.011 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".