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Record W3184209971 · doi:10.1111/1911-3846.12717

Voluntary Disclosure in Light of Control Concerns*

2021· article· en· W3184209971 on OpenAlexvenueno aff
Anil Arya, Ram Ramanan

Bibliographic record

VenueContemporary Accounting Research · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicAuction Theory and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsValuation (finance)Economic rentBusinessRevenuePrivate information retrievalIncentiveProcurementInformation asymmetryAgency costStock (firearms)MicroeconomicsControl (management)AccountingFinanceIndustrial organizationEconomicsMarketingCorporate governance

Abstract

fetched live from OpenAlex

ABSTRACT The centrality of private information in the design of accounting institutions has been explored via agency models that address control concerns as well as disclosure models that amplify valuation issues. This paper derives disclosures by an entrepreneur‐owner when both control and valuation concerns are in play. In particular, the disclosures influence stock price not only via a direct impact on valuation of the firm's revenue but also via an indirect impact on the firm's cost of procuring inputs from a self‐interested and privately informed upstream supplier. In this setting, disclosures are judiciously designed to influence the supplier's decision to share cost information and to control information rents embedded in the procurement contract within the supply chain. Specifically, in order to convey that information rents are not in the offing and, thus, motivate information sharing by the supplier, the owner has incentives to convey a less “rosy” picture. In effect, when controlling supplier actions also becomes important, the owner discloses some unfavorable revenue news that she would have otherwise withheld and conceals some favorable revenue news that she would have otherwise revealed. Consequently, in our model, the disclosure region is either two‐tailed or intermediate, in contrast to the single‐tailed disclosure region implied by familiar valuation considerations alone.

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)0.011
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.207
GPT teacher head0.467
Teacher spread0.260 · 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 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

Citations7
Published2021
Admission routes1
Has abstractyes

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