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Record W4386200828 · doi:10.37727/jkdas.2023.25.4.1391

Mutual Shareholding Limited Business Group and Quarterly Earnings Management

2023· article· en· W4386200828 on OpenAlexaboutno aff

Bibliographic record

VenueThe Korean Data Analysis Society · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAccountingCorporate groupCommissionBusinessEarningsShareholderEarnings managementQuarter (Canadian coin)Government (linguistics)FinanceCorporate governance

Abstract

fetched live from OpenAlex

This study aims to analyze the effect of a mutual shareholding limited business group system which is designated and managed by the Korean Fair-Trade Commission on corporate discretionary accounting choices. According to Watts and Zimmerman (1986), accounting numbers are often used to overcome political circumstances when companies are exposed due to government regulations. As a result of analyzing the listed companies from 2015 to 2021, it was confirmed that the size of quarterly earnings management measured by quarter-year-industry based on Kothari, Leone, and Wasley (2005)’s management performance-controlled model was statistically significantly larger for companies belonging to a mutual shareholding limited business group than those not belonging to a business group. In addition, it was found that earnings management was more aggressive in the fourth quarter than in other quarters. And these results were found to be supported regardless of the largest shareholder’s share. This study has academic significance in that it provides a deep understanding of the impact of accounting practice following the designation of a mutual shareholding limited business group system, a representative business group in Korea.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.007
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0020.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.024
GPT teacher head0.233
Teacher spread0.209 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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