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Record W3125083133

Executive Compensation, Earnings Management and Shareholder Litigation

2010· article· en· W3125083133 on OpenAlexaff
Yan Wendy Wu, Richard A. Jones

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsSimon Fraser UniversityWilfrid Laurier University
Fundersnot available
KeywordsSettlement (finance)ShareholderExecutive compensationAffect (linguistics)DismissalClass actionEarningsEarnings managementBusinessCompensation (psychology)AccountingLitigation risk analysisActuarial scienceEconomicsFinanceLawPsychologyPolitical scienceCorporate governanceSocial psychology
DOInot available

Abstract

fetched live from OpenAlex

This paper examines the effects of executive compensation and potential for earnings management on the incidence of shareholder class action lawsuits and their outcomes. Although damage measurement factors,managerial option intensity, and earnings management all significantly affect the probability of lawsuits, they differ in their influence on the likelihood of positive settlement and on settlement amount: Damage factors do not affect the likelihood of settlement versus dismissal.High option intensity raises the probability of positive settlement, but does not affect its amount.High earnings management, on the other hand, does not affect the likelihood of settlement, but does increase settlement amount.These findings suggest that factors typically used to explain shareholder lawsuits should be interpreted with care.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.012
GPT teacher head0.198
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2010
Admission routes1
Has abstractyes

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