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Record W2409096933 · doi:10.5296/ijafr.v6i1.9581

The Impact of IFRS Adoption on Accounting Conservatism in the European Union

2016· article· en· W2409096933 on OpenAlexaff
Daniel Zéghal, Zouhour Lahmar

Bibliographic record

VenueInternational Journal of Accounting and Financial Reporting · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsWilfrid Laurier UniversityInstitute on GovernanceUniversity of Ottawa
Fundersnot available
KeywordsAccountingAccrualConservatismEuropean unionOriginalitySample (material)BusinessInternational Financial Reporting StandardsEmpirical researchValue (mathematics)Empirical evidencePolitical scienceInternational tradePoliticsLawEarnings

Abstract

fetched live from OpenAlex

Purpose –The purpose of this study is to analyze mandatory IFRS adoption’s impact on accounting conservatism. Design/methodology/approach – Our empirical study is conducted on a sample of 15 European countries, observed from the year 2000 to 2010. We analyze both conditional and unconditional conservatism, which we measured, respectively, by timely bad news recognition as compared to recognition of good news and discretionary accruals. Findings – The results of the empirical study confirm a significant reduction of accounting conservatism in the IFRS adoption period. This reduction is affected by the accounting model prevailing in a particular country. Moreover, the study shows a reduction of the gap between the two accounting models in the post-IFRS adoption period. Practical implications – The results obtained would be relevant for many decision makers such as investors, standard setters, IASB, European Union countries as well as those wishing to adopt International Standards. Originality/value – Our study complements and enriches the existent literature about the impact of the International Standards adoption. It dresses an important issue in a relatively long period to better assess the impact of 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)0.010
metaresearch head score (Gemma)0.045
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.045
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.015
GPT teacher head0.260
Teacher spread0.245 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

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