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Record W2808270233 · doi:10.2308/ajpt-52155

Investor Reaction to Auditors' Going Concern Emphasis of Matter: Evidence from a Natural Experiment

2018· article· en· W2808270233 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAuditing A Journal of Practice & Theory · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAuditFinancial statementAccountingParagraphGoing concernBusinessAuditor's reportPolitical scienceLaw

Abstract

fetched live from OpenAlex

SUMMARY Using a “natural experiment” provided by a change in Canadian auditing standards requiring an emphasis of matter paragraph in the auditor's report (GC-EOM) when the financial statements include a going concern uncertainty disclosure (GC-FS), this paper examines the incremental investor reaction to the auditor's report over the related GC-FS. Conditioning on the linguistic severity of the GC-FS (weak and severe), we first document a negative price response to severe but not to weak GC-FS before the regulatory change. This implies that investors react to financial statement disclosures and account for their degree of interpretability in the absence of a GC-EOM. When the uncertainty disclosure is accompanied by a GC-EOM, we find incremental negative abnormal returns and lower abnormal trading volume only for weak GC-FS. Collectively, these findings imply that an emphasis of matter paragraph in the auditor's report can have incremental value to investors. JEL Classifications: M42; G12; G14. Data Availability: Data used are available from public sources identified in the study.

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.

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)0.062
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.062
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.005
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.283
Teacher spread0.264 · 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