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Record W2605177464 · doi:10.5296/rae.v9i1.10900

Who Really Benefits from Mandatory Adoption of IFRS? A Closer Look at Preparers and Users of Financial Information

2017· article· en· W2605177464 on OpenAlexaff
Michel Sayumwe, Claude Francœur

Bibliographic record

VenueResearch in Applied Economics · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsHEC MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsBusinessAccountingCreditorInternational Financial Reporting StandardsCapital marketAccounting information systemSample (material)EarningsNoticeEuropean unionFinanceDebt

Abstract

fetched live from OpenAlex

Since 1 January 2005, the European Union (EU) has mandated implementation of International Financial Reporting Standards (IFRS) for preparation of consolidated financial statements for EU-listed firms. This paper analyzes the economic impact of mandatory adoption of IFRS on firms, investors, and creditors. Relying on the positive accounting theory, we study the economic impact of the new conceptual framework of financial information from IASB (2010), especially in paragraph OB2, where investors and creditors are designated as the main users of financial information, and QC 38, which assesses whether the benefits of financial information justify the costs associated with its production and use. Our sample is composed of 2,926 European firms that adopted IFRS in 2005. Results show that firm’s cost of capital declines when comparing data before and after IFRS adoption. For creditors, our results suggest that credit rating improves after IFRS adoption. However, we do not notice any significant difference in the quality of accounting earnings for investors.We also test if these results hold in the presence of asymmetric information, financial dependence and family ownership structure. Our results confirm the above trend. We conclude that the market anticipates the content of accounting data, and that mandatory adoption of IFRS has no impact on investors.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.522
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.001
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 head0.026
GPT teacher head0.254
Teacher spread0.228 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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