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Record W2028108141 · doi:10.1111/1911-3838.12011

The Impact of Disclosures of Internal Control Weaknesses and Remediations on Investors' Perceptions of Earnings Quality

2013· article· en· W2028108141 on OpenAlexaffvenue
He Luo, Daniel B. Thornton

Bibliographic record

VenueAccounting Perspectives · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsQueen's UniversityConcordia University
Fundersnot available
KeywordsAccountingBusinessAuditControl (management)Earnings qualityQuality (philosophy)EarningsStrengths and weaknessesPerceptionOrder (exchange)Representation (politics)FinanceEconomicsAccrualPsychology

Abstract

fetched live from OpenAlex

Abstract We hypothesize and find that firms making SOX‐mandated disclosures of material weaknesses in internal control over financial reporting (ICOFR) exhibit lower investor‐perceived earnings quality (IPEQ) than nondisclosers. We measure IPEQ using e‐loading, a market‐returns–based representation of earnings quality developed by Ecker, Francis, Kim, Olsson, and Schipper (2006). Firms do not exhibit decreases in IPEQ after initially disclosing material weaknesses. This is consistent with investors having anticipated ICOFR strength based on observable firm characteristics. However, firms exhibit increases in IPEQ after receiving their first clean audit reports that confirm the remediation of previously disclosed weaknesses. This indicates that, although investors do not find initial weakness disclosures to be incrementally informative, SOX motivates firms to remediate weak controls and provides a venue for credible remediation disclosures, thus enhancing investors' perception of financial reporting reliability. These findings are consistent with the existence of regulatory benefits associated with SOX's internal control disclosure and audit requirements.

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)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.261
Teacher spread0.253 · 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

Citations5
Published2013
Admission routes2
Has abstractyes

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