Theoretical Profiles for the Evaluation of Insider Trading in a Functional Model of Financial Instruments Market
Bibliographic record
Abstract
The analysis is based on the premise that the capital market is characterized by weak forms of risk management, to be intended, in this case, as risk of information asymmetry as well as operational inefficiency, as there are no hedging schemes to prevent external actions and internal mechanisms are not inspired by adequate transparency principles.After a critical review of the theoretical effects of insider trading, starting with a market equilibrium assessment, this analysis seeks to demonstrate the absence of any positive effect linked to insider trading in relation to any type of variable and for any model of the securities market.Starting from the assumption that the negative trading activity of insiders manifests in any securities market structure, it has been shown that an operating model characterized by the presence of professional operators appears to be more capable of opposing a significant barrier to the entry of insiders.On the other hand, it has also been shown that the presence of professional operators cannot act alone and it may also lose action incisiveness and even cause informative viscosity effect, when such professional or institutional operators are directly involved in privatization operations.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".