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Record W3112613052 · doi:10.1111/jbfa.12514

Universal demand laws and the monitoring demand for accounting conservatism

2020· article· en· W3112613052 on OpenAlexafffund
Feng Chen, Qingyuan Li, Li Xu

Bibliographic record

VenueJournal of Business Finance &amp Accounting · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaNational Natural Science Foundation of China
KeywordsFiduciaryShareholderConservatismCorporate governanceIncentiveAccountingBusinessLitigation risk analysisEquity (law)Corporate lawEconomicsLawLaw and economicsAuditFinanceMarket economyPolitical scienceDuty

Abstract

fetched live from OpenAlex

Abstract While prior research holds the consensus that accounting conservatism can serve as an effective monitoring device, it is not clear whether shareholders can successfully enforce managers’ adherence to accounting conservatism when directors fail to fulfill their fiduciary duties. We attempt to answer the question by exploiting staggered enactments of the universal demand (UD) laws in 23 US states. UD laws raise procedural hurdles for shareholders to file derivative lawsuits against managers and directors who allegedly breach their fiduciary duties. For firms incorporated in states that adopt UD laws, restrictions on shareholder litigation rights weaken directors’ incentives to monitor managers. We predict and find a decrease in conditional conservatism following the enactment of UD laws. The decline in conditional conservatism exists only for firms with low institutional ownership, low external equity dependence, or high ex‐ante derivative lawsuit risk. The main result is attributable to both the direct channel (through restriction of shareholder litigation rights) and the indirect channel (through the deployment of management‐friendly governance provisions). Our findings suggest that shareholders cannot successfully demand accounting conservatism when directors lack the incentives to monitor managers.

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)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.004
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 head0.017
GPT teacher head0.215
Teacher spread0.198 · 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.

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

Citations17
Published2020
Admission routes2
Has abstractyes

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