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Record W2049997523 · doi:10.1111/1911-3838.12002

Information Quality of Interim Financial Statements

2012· article· en· W2049997523 on OpenAlexvenueaboutno aff
Karen Lightstone, Nicola M. Young, Tyra McFadden

Bibliographic record

VenueAccounting Perspectives · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)InterimAccountingBusinessVolatility (finance)EarningsAuditFinanceNet incomeEconomicsActuarial science

Abstract

fetched live from OpenAlex

Abstract Expressing concern about the Canadian capital market environment, Boritz (2006) suggested that the accounting and auditing profession may be paying limited attention to quarterly reports. This study investigates whether fourth‐quarter adjustments are significantly different from the previous three, thereby limiting the reliability or faithful representation of the firms' results for each quarter. This study includes four years (2003–2006) of quarterly financial information of 353 Canadian public companies. Our results indicate that the volatility of net income in each of the first three quarters is considerably lower than in the final quarter. While lower volatility can improve predictability, the resulting relevance may be limited. The low volatility of reported earnings in the first three quarters suggests that either earnings management is taking place or that management may not be exercising sufficient care at the end of each of the first three quarters on the measurements that generally accepted accounting principles call for and readers of financial statements expect. This could result in quarterly financial statements that do not faithfully represent the underlying resources and obligations of the reporting firms at the end of the quarter, or the firm's performance during the quarter. Our findings support Boritz's proposition for increased audit requirements for interim reports and changes in the approach to the annual audit to integrate it more closely with interim financial reporting.

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)0.034
metaresearch head score (Gemma)0.218
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 score0.088
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.218
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.021
GPT teacher head0.297
Teacher spread0.276 · 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

Citations13
Published2012
Admission routes2
Has abstractyes

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