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Record W3197809612 · doi:10.1108/mf-08-2020-0442

The effect of firm-specific litigation risk on independent director conservatism

2021· article· en· W3197809612 on OpenAlexaff
Guoping Liu, Jerry Sun

Bibliographic record

VenueManagerial Finance · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of WindsorToronto Metropolitan University
Fundersnot available
KeywordsCorporate governanceAccountingBusinessConservatismLiabilityEvent studyLitigation risk analysisDamagesAccrualIndependence (probability theory)Shock (circulatory)Actuarial scienceEconomicsFinanceLawAuditEarningsPolitical science

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to examine whether firm-specific litigation risk affects independent director conservatism in the oversight of financial reporting. Design/methodology/approach This study considers the enactment of Sarbanes–Oxley Act and the main US stock exchanges' corresponding corporate governance regulations in 2002–2003 as an exogenous shock event to increase board independence. OLS regressions with fixed effects are conducted to test the hypothesis. Findings Changes in discretionary accruals from the pre-event year (2001) to the post-event year (2004) are more negatively associated with an exogenous increase in board independence for firms with high litigation risk than for firms with low litigation risk. Originality/value The results suggest that independent directors are more conservative in overseeing financial reporting when they face higher litigation risk, consistent with the notion that they are still concerned about liability risk although they seldom have to pay damages or legal fees out of their own pockets.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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 designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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