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Record W2002379648 · doi:10.5430/afr.v4n1p17

Determinants of Dividend Policy: Evidence from GCC Market

2014· article· en· W2002379648 on OpenAlexvenueno aff
Rajesh Kumar

Bibliographic record

VenueAccounting and Finance Research · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsDividend policyDividendMarket liquidityDividend payout ratioPecking order theoryOrder (exchange)EconomicsRetained earningsCapital structureMonetary economicsCapital marketFinancial economicsExternal financingDividend yieldBusinessFinanceDebt

Abstract

fetched live from OpenAlex

This paper examines the determinants of dividend policy in GCC market based on sample firms in UAE market. An analysis was conducted to understand the dividend trends among different industry sectors in UAE market. The analysis of approximately 120 listed companies reveal that 80 per cent of the companies paid cash dividends during the three year period 2011-2013. The paper also examines the various theoretical attributes used in financial literature to understand the determinants of dividend policy. The partial least squares structural equations modeling (PLS-SEM) was used to test the alternate explanations of corporate dividend payout policy in the gulf market. The study finds support for residual theory and pecking order argument of dividends. Investment policy influences dividend policy. The results support the theory that firms with high growth rate in income requires higher capital expenditure and establish lower dividend payout on account of costly external financing. Liquidity is an important determinant of dividend decision. Stability of dividend payments is not a critical factor considered by financial markets in the region.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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)0.0020.000

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 head0.063
GPT teacher head0.325
Teacher spread0.262 · 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

Citations18
Published2014
Admission routes1
Has abstractyes

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