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Record W3123327626 · doi:10.1177/0148558x0902400203

Impact of Excess Auditor Remuneration on the Cost of Equity Capital around the World

2009· article· en· W3123327626 on OpenAlexafffund
Ole‐Kristian Hope, Tony Kang, Wayne B. Thomas, Yong Keun Yoo

Bibliographic record

VenueJournal of Accounting Auditing & Finance · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Toronto
FundersSingapore Management UniversitySocial Sciences and Humanities Research Council of CanadaMinistry of Education and Human Resources Development
KeywordsRemunerationAuditor independenceBusinessEquity (law)Inherent risk (accounting)AccountingAuditCost of capitalMonetary economicsCapital marketFinanceEconomicsExternal auditorJoint auditInternal audit

Abstract

fetched live from OpenAlex

This study examines the relation between excess auditor remuneration and the implied required rate of return (IRR hereafter) on equity capital in global markets. We conjecture that when auditor remuneration is excessively large, investors may perceive the auditor to be economically bonded to the client, leading to a lack of independence. This perceived lack of independence increases the information risk associated with the credibility of financial statements, thereby increasing IRR. Consistent with this notion, we find that IRR is increasing in excess auditor remuneration, but only in countries with stronger investor protection. Finding evidence of a relation only in stronger investor protection countries is consistent with the more prominent role of audited financial statements for investors' decisions in these countries. In settings in which investors are less likely to rely on audited financial statements and instead rely on alternative sources of information (i.e., in countries with weaker investor protection), the impact of client-auditor bonding should have less of an effect on investors' decisions.

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)0.003
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.272
Teacher spread0.255 · 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

Citations98
Published2009
Admission routes2
Has abstractyes

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