MétaCan
Menu
Back to cohort
Record W1603103851 · doi:10.1108/18347641111105917

IFRS and US‐GAAP comparability before release No. 33‐8879

2011· article· en· W1603103851 on OpenAlexaff
Chunhui Liu

Bibliographic record

VenueInternational Journal of Accounting and Information Management · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsComparabilityAccountingNet incomeInternational Financial Reporting StandardsBusinessAccounting standardSample (material)Financial accountingAccounting information system

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to investigate whether reported net income as per US‐generally accepted accounting principles (US‐GAAP) has become comparable to net income as per International Financial Reporting Standards (IFRS) as issued by the International Accounting Standards Board right before the removal of the US‐GAAP reconciliation requirement and what major accounting elements have caused the differences, if any. Design/methodology/approach Using Gray's index of comparability suggested by Haverty, the paper compares the reported net income under IFRS for a sample of US‐listed Chinese companies using IFRS with the reconciled net income under US‐GAAP. Findings Consistent with Haverty is the finding that net income under IFRS is still not completely comparable to net income under US‐GAAP for the same company and that the adjustment for tangible assets revaluation is a major contributor to the difference. In addition, different treatment of business acquisition is found to be another major cause of the incomparability. The comparability has improved at 10 percent threshold since Haverty's study. Originality/value This paper provides an update on the status of IFRS and US‐GAAP comparability and highlights an additional major area to work on towards improved comparability.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.009
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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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

Citations38
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Accounting and Information ManagementSame topicAuditing, Earnings Management, GovernanceFrench-language works237,207