MétaCan
Menu
Back to cohort
Record W2014945053 · doi:10.1142/s0219091511500081

Value Relevance Change Under International Accounting Standards: An Empirical Study of Peru

2012· article· en· W2014945053 on OpenAlexaff
Chunhui Liu, Lee J. Yao, Michelle Y.M. Yao

Bibliographic record

VenueReview of Pacific Basin Financial Markets and Policies · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Winnipeg
FundersInstitute for Advanced Studies in Basic SciencesEuropean Commission
KeywordsAccountingInternational Financial Reporting StandardsAccounting standardFair valueBusinessHistorical costFinancial accountingRelevance (law)Mark-to-market accountingDiscretionCommissionSample (material)Value (mathematics)Quality (philosophy)Accounting information systemFinancePolitical science

Abstract

fetched live from OpenAlex

In face of broad adoption of International Financial Reporting Standards (IFRS), the Securities and Exchange Commission (SEC) is considering its quality and acceptability. This paper reports a study that examines changes in value relevance with a sample of Peru firms mandated to use international accounting standards between 1999 and 2007. The period under study is broken into a period of International Accounting Standards (IAS) between 1999 and 2001, a period of early IFRS between 2002 and 2004, and a more recent period of IFRS between 2005 and 2007 by major changes to accounting standards. The empirical results generally indicate that value relevance improved from the IAS period to the early IFRS period when the International Accounting Standards Board (IASB) took over the International Accounting Standards Committee (IASC), but worsened from the early IFRS period to the recent IFRS period when more accounting standards started to reflect IASB's preference for fair value measurement of assets and liabilities. Quality weakens to a greater extent for firms with more discretion for fair value estimates. Further analysis shows that such changes are less likely to result from changes in economic conditions, but from the changes of the standards. The findings are particularly alarming in face of rising IFRS adoptions and call for quality improvement to IFRS.

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)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.464
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.301
Teacher spread0.276 · 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 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

Citations19
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueReview of Pacific Basin Financial Markets and PoliciesSame topicAuditing, Earnings Management, GovernanceFrench-language works237,207