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Record W3011505042 · doi:10.1111/1911-3846.12486

Impact of Auditor Report Changes on Financial Reporting Quality and Audit Costs: Evidence from the United Kingdom

2019· article· en· W3011505042 on OpenAlexvenueno aff
Lauren C. Reid, Joseph V. Carcello, Chan Li, Terry L. Neal, Jere R. Francis

Bibliographic record

VenueContemporary Accounting Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAuditAccountingAccrualBusinessQuality auditAuditor independenceAudit evidenceExternal auditorEarningsJoint auditQuality (philosophy)Auditor's reportInternal audit

Abstract

fetched live from OpenAlex

ABSTRACT While substantial revisions to auditor reporting requirements are being implemented internationally, the impact of these reforms on financial reporting quality is unknown. We exploit the United Kingdom's recent auditor reporting changes and find that the United Kingdom's new reporting regime is associated with an improvement in financial reporting quality as proxied by significant decreases in absolute abnormal accruals and the propensity to just meet or beat analyst forecasts, and a significant increase in earnings response coefficients. As for audit costs, we do not find a significant change in audit fees or audit delay surrounding the implementation of the new reporting regime. Taken together, the results of this study suggest that new auditor reporting requirements are associated with a significant improvement in financial reporting quality without detecting a significant increase in audit costs.

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)0.008
metaresearch head score (Gemma)0.075
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 score0.110
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.075
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.151
GPT teacher head0.389
Teacher spread0.238 · 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

Citations352
Published2019
Admission routes1
Has abstractyes

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