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Record W2470923548 · doi:10.1177/0312896216641600

Does the market price the nature and extent of earnings management for firms that beat their earnings benchmark?

2016· article· en· W2470923548 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.

Bibliographic record

VenueAustralian Journal of Management · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsLakehead University
Fundersnot available
KeywordsAccrualEarningsEarnings managementPost-earnings-announcement driftEarnings response coefficientEconomicsBusinessSample (material)Price–earnings ratioFinancial economicsMonetary economicsEarnings per shareAccounting

Abstract

fetched live from OpenAlex

This study investigates whether the abnormal returns at the quarterly earnings announcement date varies according to the market’s expectations of the nature (informative vs opportunistic) and extent of discretionary accruals for firms that meet or beat expectations (MBE). In doing so, this study introduces an innovative model that measures the market’s expectation of the informativeness of earnings at the earnings announcement date and assesses the impact on the abnormal return for the interaction between the nature and expected extent of earnings management. A large sample of Standard & Poor’s (S&P) 500 firms that meet or exceed their earnings expectation over the period of 1998 to 2007 is analyzed. The results reveal that the expected extent of earnings management has a positive (negative) relation with the abnormal return when earnings management is informative (opportunistic).

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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