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Record W3123800983 · doi:10.5539/ibr.v3n3p210

Ownership Structure and Cash Flows As Determinants of Corporate Dividend Policy in Pakistan

2010· article· en· W3123800983 on OpenAlexvenueno aff
Talat Afza, Hammad Hassan Mirza

Bibliographic record

VenueInternational Business Research · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsDividend policyLeverage (statistics)DividendFree cash flowCash flowStock exchangeProfitability indexBusinessMonetary economicsEconomicsEnterprise valueDividend payout ratioFinancial economicsAccountingFinanceStatisticsMathematics

Abstract

fetched live from OpenAlex

Dividend Policy is among the widely addressed topics in modern financial literature. The inconclusiveness of the theories on importance of dividend in determining firm’s value has made it one of the most debatable topics for the researchers (see for example, Ramcharan, 2001; Frankfurter et. al 2002; Al-Malkawi, 2007). The present study investigates the impact of firm specific characteristics on corporate dividend behavior in emerging economy of Pakistan. Three years data (2005-2007) of 100 companies listed at Karachi Stock Exchange (KSE) has been analyzed using Ordinary Least Square (OLS) regression. The results show that managerial and individual ownership, cash flow sensitivity, size and leverage are negatively whereas, operating cash-flow and profitability are positively related to cash dividend. Managerial ownership, individual ownership, operating cash flow and size are the most significant determinants of dividend behavior whereas, leverage and cash flow sensitivity do not contribute significantly in determining the level of corporate dividend payment in the firms studied in our sample. Estimated results are robust to alternative proxy of dividend behavior i.e. dividend intensity.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.011
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.063
GPT teacher head0.363
Teacher spread0.300 · 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.

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

Citations111
Published2010
Admission routes1
Has abstractyes

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