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Signaling Firm Performance Through Financial Statement Presentation: An Analysis Using Special Items

2010· article· en· W2330104029 on OpenAlexvenueno aff
Edward J. Riedl, Suraj Srinivasan

Bibliographic record

VenueContemporary Accounting Research · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsPresentation (obstetrics)Statement (logic)Financial statement analysisIncome statementFinancial statementAccountingStatement of changes in financial positionEarningsPro formaOpportunismActuarial sciencePsychologyBusinessFinancial analysisFinancial accountingEconomicsBalance sheetPolitical scienceAccounting information systemLawMedicine

Abstract

fetched live from OpenAlex

This paper investigates whether managers’ presentation of special items within the financial statements reflects economic performance or opportunism. Specifically, we assess special items presented as a separate line item on the income statement (income statement presentation) to those aggregated within another line item with disclosure only in the footnotes (footnote presentation). Our study is motivated by standard‐setting interest in performance reporting and financial statement presentation, as well as prior research investigating managers’ presentation choices in other contexts. Empirical results reveal that special items receiving income statement presentation are less persistent relative to those receiving footnote presentation. These results are consistent across numerous alternative specifications. Overall, the findings are consistent with managers using the income statement versus footnote presentation to assist users in identifying those special items most likely to differ from other components of earnings — that is, for informational, as opposed to opportunistic, motivations.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0020.010
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.205
GPT teacher head0.400
Teacher spread0.195 · 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.

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

Citations3
Published2010
Admission routes1
Has abstractyes

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