MétaCan
Menu
Back to cohort
Record W2132048472 · doi:10.5539/ibr.v6n7p32

Accounting Conservatism and Information Asymmetry: Evidence from Taiwan

2013· article· en· W2132048472 on OpenAlexvenueno aff
Juo-Lien Wang

Bibliographic record

VenueInternational Business Research · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsConservatismEarningsInformation asymmetryAccountingCorporationEmpirical evidenceEconomicsNeutralityEmpirical researchEarnings response coefficientBusinessFinancial economicsPolitical scienceFinanceLawPolitics

Abstract

fetched live from OpenAlex

This paper adopts information neutrality perspective to examine the role of earnings conservatism to explore the effect of accounting conservatism on information asymmetry in Taiwan. Results reveal that when corporate earnings are increasingly conservative, information asymmetry is more severe generally. In addition, when earnings conservatism is excessive or insufficient, varying effects are produced between earnings conservatism levels and information asymmetry. Specifically, when corporate conservatism is insufficient, the relationship between earnings conservatism and information asymmetry is significantly negative. Conversely, when accounting earnings is much more conservative, the influence on information asymmetry is positive. The empirical results of this study support the current development of financial standards. The empirical results also show that when the corporation performance demonstrates good news or bad news separately, investors’ perception regarding the informativeness of accounting conservatism is different. Investors are more likely to identify with relevant results despite an overly conservative accounting recognition if the current period of a corporation provides good news.

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)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesScholarly communication, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0030.016
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.004

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 head0.034
GPT teacher head0.295
Teacher spread0.260 · 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 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

Citations17
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueInternational Business ResearchSame topicAuditing, Earnings Management, GovernanceFrench-language works237,207