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Record W3154469459 · doi:10.1080/17449480.2021.1900581

The Value Relevance of Fair Value Levels: Time Trends under IFRS and U.S. GAAP

2021· article· en· W3154469459 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAccounting in Europe · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsCenter for Interuniversity Research and Analysis on OrganizationsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsValue (mathematics)AccountingRelevance (law)Fair valueBusinessEconomicsPolitical scienceMathematicsStatisticsLaw

Abstract

fetched live from OpenAlex

The IASB's post-implementation review of IFRS 13 Fair Value Measurement motivates our analysis of the evolution of the value relevance of fair value (FV) levels over time on banks that report under IFRS and U.S. GAAP. For both sets of standards, results provide evidence that is consistent with (1) an increase in value relevance across all three FV levels over time, and (2) a convergence of the value relevance of the three FV levels over time. However, FV levels exhibit systematically higher value relevance under U.S. GAAP compared to IFRS. Such gap has closed to some extent since the enactment of IFRS 13. This evolution is likely due to learning about FV accounting and changes in financial reporting regulations that increased disclosure requirements. These findings confirm the IASB's conclusions that FV levels’ disclosure is useful to users of financial statements, but also emphasizes preparers and investors’ learning over time.

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)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.011
GPT teacher head0.215
Teacher spread0.204 · 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