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Record W2149629118 · doi:10.5430/afr.v2n4p130

IFRS Adoption in Italy: Which Effects on Accounting Figures and Subjectivity?

2013· article· en· W2149629118 on OpenAlexvenueno aff
Silvano Corbella, Cristina Florio, Francesca Rossignoli

Bibliographic record

VenueAccounting and Finance Research · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsSubjectivityAccountingBusinessEconomics

Abstract

fetched live from OpenAlex

The issue about the degree of subjectivity incidental to financial statements is topical, although it has long been studied and debated. Indeed, such issue recurs anytime new accounting rules or standards are issued. And, of course, it recurs in case of a complete renovation of the accounting system of reference, such as the one that took place in Europe in 2005, when all listed companies were required to repeal their national accounting rules and GAAP, and to adopt IFRS for the preparation of their consolidated financial statements. This study focuses on such transition with specific reference to the Italian context and explores three interconnected issues: a)identification of the changes in the evaluation criteria – from the Italian regulations and GAAP to IFRS – which actually impact on the financial statements presented by Italian companies in the year of transition; b) appreciation of the importance of such impacts, based on (1) how often each adjustment recurs; (2) whether they determine an increase or decrease of accounting figures and (3) how relevant are their effects on the main accounting figures, namely net earnings and net capital; c) discussion about the managerial discretion introduced by the most impacting evaluation criteria identified, in comparison with the Italian provisions and GAAP previously applied. The overall analysis demonstrates that IFRS introduction determined wide impacts on financial statements, affecting most assets and liabilities, but its impacts on accounting figures were less significant than could be expected. In terms ofsubjectivity, however, differences are very significant.

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)0.003
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0000.001
Research integrity0.0000.001
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

Citations6
Published2013
Admission routes1
Has abstractyes

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