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Decoding Inside Information

2012· preprint· en· W2890190386 on OpenAlexaff
Lauren Cohen, Christopher J. Malloy, Łukasz Pomorski

Bibliographic record

VenueThe Journal of Finance · 2012
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInsiderInsider tradingPortfolioPredictive powerSet (abstract data type)Decoding methodsUniverseVariety (cybernetics)BusinessValue (mathematics)Financial economicsEconometricsEconomicsActuarial scienceComputer scienceFinanceArtificial intelligenceAlgorithmMachine learningPolitical scienceLaw

Abstract

fetched live from OpenAlex

ABSTRACT Exploiting the fact that insiders trade for a variety of reasons, we show that there is predictable, identifiable “routine” insider trading that is not informative about firms’ futures. A portfolio strategy that focuses solely on the remaining “opportunistic” traders yields value‐weighted abnormal returns of 82 basis points per month, while abnormal returns associated with routine traders are essentially zero. The most informed opportunistic traders are local, nonexecutive insiders from geographically concentrated, poorly governed firms. Opportunistic traders are significantly more likely to have SEC enforcement action taken against them, and reduce trading following waves of SEC insider trading enforcement.

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)0.000
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 score0.534
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.045
GPT teacher head0.227
Teacher spread0.182 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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