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Record W7119384153

La evaluación de la value relevancede las informaciones acerca la jerarquía del valor razonable de las compañías brasileñas

2017· article· pt· W7119384153 on OpenAlexaboutno aff
Tatiane de Oliveira Marques, Jorge Katsumi Niyama, Rafael Morais de Souza, Charles Albino Schultz

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2017
Typearticle
Languagept
FieldBusiness, Management and Accounting
TopicFinancial Reporting and Valuation Research
Canadian institutionsnot available
Fundersnot available
KeywordsRelevance (law)Value (mathematics)LiabilityQuarter (Canadian coin)Variable (mathematics)Index (typography)
DOInot available

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate whether the value relevance of fair value Level 1 and 2 is greater than the value relevance of Level 3. This is a descriptive research, quantitative character and data analysis was developed based on the linear regressionmethod. Thesample consists of 50 companies listed in the Index IBrx-100 BM&FBOVESPAthat presentedinformation on the fair value hierarchy.The data collection was conduced in the quarterly financial statementsand explanatory notesfromthe first quarter of 2013to the fourth quarterof 2014. The results for assets at fair value showed that the only variable AVJ3(1,05)have value relevance,positive and statistically significantcoefficient. For liability, PVJ12 (7,64) and PVJ3 (6,92) had relevance value, but the different coefficient of the theoretically expected negative. Responding to the main researchobjective, it is possible to affirm that the value relevance of Levels 1 and 2 is greater than value relevance of Level 3for liabilities. From the results it can also be inferred that investors interpreted the liability amounts to fair valuewrongly, as had already been observed in studies of Gaynor, McDaniel and Yohn (2009) andLachmann, Wöhrmann and Wömpener (2011).

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)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.000
Scholarly communication0.0050.003
Open science0.0020.001
Research integrity0.0020.002
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 head0.042
GPT teacher head0.319
Teacher spread0.277 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations0
Published2017
Admission routes1
Has abstractyes

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Same venueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)Same topicFinancial Reporting and Valuation ResearchFrench-language works237,207