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Record W2755525343 · doi:10.5430/afr.v6n4p115

Corporate Governance Practices and Firm’s Capital Structure Decisions: An Empirical Evidence of An Emerging Economy

2017· article· en· W2755525343 on OpenAlexvenueno aff
Hassan M. Hafez

Bibliographic record

VenueAccounting and Finance Research · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceCapital structureBusinessHausman testAccountingCapital (architecture)Sample (material)Panel dataRestructuringEmerging marketsEconomicsFinanceFixed effects modelEconometrics

Abstract

fetched live from OpenAlex

There is a growing body of literature that recognises the importance of corporate governance practices on capital structure decisions. However, results are not consistent and only a few studies have been able to draw on any systematic research trying to quantify the relation between corporate governance practices and capital structure decisions and to acquire bits of knowledge of such relation of listed firms in Emerging Economies. Because of the fact that impact use of the corporate governance rules can have on capital structure decisions.The main driver of this research is to investigate the sound use of the Egyptian corporate governance practices on capital structure decisions of listed firms in Egypt over the period 2007 to 2016 utilizing a sample of 50 listed firms in EGX 100. Empirical results demonstrate the significant relationship between various inner and outer corporate governance practices and capital structure decisions of listed firms in Egypt. The findings were quantitatively approved through utilizing E-Views programming for examining panel data. Descriptive statistics, Multi-Collinearity test, Hausman test and multiple regression have been utilized to distinguish the major determinates of capital structure decisions and assess whether it has a significant impact on capital structure decisions.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, 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 score0.077
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.012
Open science0.0010.001
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 head0.215
GPT teacher head0.403
Teacher spread0.188 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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