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Record W1995928950 · doi:10.2308/accr.2006.81.5.1017

The Information Content of SEC Filings and Information Environment: A Variance Decomposition Analysis

2006· article· en· W1995928950 on OpenAlexaff
Jeffrey L. Callen, Joshua Livnat, Dan Segal

Bibliographic record

VenueThe Accounting Review · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCash flowAccrualVolatility (finance)EarningsVariance (accounting)Variance decomposition of forecast errorsEconometricsBusinessEconomicsAccountingActuarial science

Abstract

fetched live from OpenAlex

Using the Vuolteenaho (2002) variance decomposition methodology, this study assesses the relative value relevance of cash flow, accrual, and expected return news on SEC and preliminary earnings filing dates, as measured by their contribution to the volatility of unexpected returns. Cash flow news is found to be more valuerelevant than accrual news. Although expected return (risk) news is the least valuerelevant, it is significantly correlated with changes in betas and returns at the preliminary and SEC filing dates, indicating association with changes in firm risk and discount rates. This study also documents that these informational components contain less (more) value-relevant information at the SEC filing date for firms with a higher proportion of long-term (short-term) sophisticated investors after controlling for other dimensions of the information environment.

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)0.002
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: none
Teacher disagreement score0.785
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.192
Teacher spread0.186 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations83
Published2006
Admission routes1
Has abstractyes

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