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Record W2771616880 · doi:10.5430/ijba.v9n1p1

Theoretical Profiles for the Evaluation of Insider Trading in a Functional Model of Financial Instruments Market

2017· article· en· W2771616880 on OpenAlexvenueno aff
Paola Fandella

Bibliographic record

VenueInternational Journal of Business Administration · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsInsider tradingInefficiencyInsiderTransparency (behavior)Capital marketBusinessPremiseInformation asymmetryEconomicsMarket manipulationMicroeconomicsAccountingFinancial economicsMonetary economicsFinanceComputer scienceLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.108
GPT teacher head0.305
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2017
Admission routes1
Has abstractyes

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