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Record W4226061747 · doi:10.2308/tar-2019-0521

Evolution in Value Relevance of Accounting Information

2022· article· en· W4226061747 on OpenAlexaff
Mary E. Barth, Ken Li, Charles McClure

Bibliographic record

VenueThe Accounting Review · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsMcMaster University
Fundersnot available
KeywordsValuation (finance)EarningsRelevance (law)EconomicsAccounting information systemValue (mathematics)AccountingFair valueBook valuePolitical science

Abstract

fetched live from OpenAlex

ABSTRACT We address how value relevance of accounting information evolved as the new economy developed. Prior research concludes that accounting information—primarily earnings—has lost relevance. We consider more accounting items and find no decline in combined value relevance from 1962 to 2018. We assess evolution in each item’s value relevance and find increases, most notably for items related to intangible assets, growth opportunities, and alternative performance measures, which are important in the new economy. The number of relevant items also increases. We also consider separately new economy, old economy profit, and old economy loss firms. The trends are more pronounced for, but extend beyond, new economy firms. We base inferences on a nonparametric approach that does not require specifying the valuation relation. Taken together, our findings reveal an evolution to a more nuanced, but not declining, relation between accounting information and share price. JEL Classifications: C14; G10; G18; M40; M41.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.074
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.074
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.007
Science and technology studies0.0000.001
Scholarly communication0.0040.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.219
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations351
Published2022
Admission routes1
Has abstractyes

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