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Record W4292544786 · doi:10.1007/s11142-022-09703-2

Real-time revenue and firm disclosure

2022· article· en· W4292544786 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueReview of Accounting Studies · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
FundersNanyang Technological UniversityUniversity of AlbertaIowa State UniversityCarnegie Mellon UniversityUniversity of Oklahoma
KeywordsRevenueEarningsBusinessDiscretionDatabase transactionInsiderMonetary economicsRevenue recognitionInsider tradingCorporate financeStock (firearms)FinanceAccountingEconomicsAccounting information system

Abstract

fetched live from OpenAlex

Abstract We examine firm disclosure choice when information is received on a real-time, continuous basis. We use transaction-level credit and debit card sales for a sample of retail firms to construct a weekly measure of abnormal revenue for each firm. We validate the informativeness of this abnormal real-time revenue information, confirming its positive correlation with abnormal returns, unexpected revenue realizations, and management revenue forecast news. Using revenue forecasts, we find that firms are less likely to disclose abnormally negative news early in the quarter. As the quarter progresses, firms reduce their withholding of negative news. These results are consistent with impending earnings announcements disciplining managers to provide negative news. This pattern of initial withholding and then disclosure exists primarily in firms with high analyst coverage, high institutional ownership, or high litigation risk. Finally, we find increased insider stock sales in weeks with abnormally negative news and no firm disclosure. Overall, our study provides evidence of the informativeness of real-time information and manager discretion in its release.

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)0.002
metaresearch head score (Gemma)0.006
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.002
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 head0.013
GPT teacher head0.254
Teacher spread0.241 · 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