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Record W1556283372 · doi:10.51400/2709-6998.2046

ANALYSIS OF BOARD STRUCTURE,CORPORATE VALUE AND FINANCIAL POLICY

2007· article· en· W1556283372 on OpenAlexaboutno aff
Yu-Chen Tu, Wei-hung Lai, Heng-Chih Chou

Bibliographic record

VenueJournal of marine science and technology · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsSimultaneous equationsAccountingEnterprise valueQuarter (Canadian coin)Structural equation modelingSimultaneous equations modelValue (mathematics)EconometricsOrdinary least squaresEconomicsBusinessMathematicsStatisticsGeographyDifferential equation

Abstract

fetched live from OpenAlex

The purpose of this paper is to examine how board structure relates to corporate value and financial policy of firms in Taiwan. Using quarterly data from ten stock-listed department stores in Taiwan during the period 2000-2005, this study builds a structural model with three equation sets, and then applies three-stage least squares (3SLS) to estimate all equations in the model simultaneously. It should be emphasized that all empirical deviations of normal asymptotic properties caused by OLS or 2SLS in the previous studies can be improved by 3SLS, even though the model of this study contains lagged endogenous variables. The empirical results finally show that most factors of board structure affect significantly corporate value and financial policy in the current period, except for the factor of Internal Board Shareholding Ratio with the significant effects in a time lag of one quarter.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.224
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2007
Admission routes1
Has abstractyes

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