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Unexpected Accruals and Conditional Accounting Conservatism

2007· article· en· W2020845611 on OpenAlex
Jinhan Pae

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.

Bibliographic record

VenueJournal of Business Finance &amp Accounting · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsQueen's University
Fundersnot available
KeywordsAccrualConservatismCash flowAccountingEarningsEarnings managementEconometricsEconomicsPolitical science

Abstract

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Abstract: This paper examines the impact of management discretion over accruals on conditional accounting conservatism, defined as the tendency of accountants to recognize bad news on a timelier basis than good news. Prior research suggests that conditional accounting conservatism reflected in earnings is mainly due to the accrual component of earnings, not the cash flow component of earnings. After decomposing total accruals into expected and unexpected accruals, I find that (1) conditional accounting conservatism reflected in accruals is mainly due to unexpected accruals; (2) the negative association between unconditional and conditional accounting conservatism is mainly attributable to unexpected accruals; and (3) firms with higher leverage exhibit conditionally more conservative accounting primarily through unexpected accruals. These results are robust to accrual models that take into account the systematic association between accruals and cash flows and their non‐linearity and to the asymmetric persistence of earnings changes specification of conditional accounting conservatism. Taken together, these results suggest that managers exercise their discretion over accruals to expedite the recognition of bad news rather than good news.

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)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.006
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 head0.013
GPT teacher head0.228
Teacher spread0.215 · 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