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Record W1491416207 · doi:10.1111/jbfa.12005

Why Firms Do Not Pay Dividends: The Canadian Experience

2012· article· en· W1491416207 on OpenAlexaboutno aff
H. Kent Baker, Bin Chang, Shantanu Dutta, Samir Saadi

Bibliographic record

VenueJournal of Business Finance &amp Accounting · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsDividendDividend policyProfitability indexBusinessCashStock (firearms)Order (exchange)Monetary economicsEconomicsFinancial economicsFinance

Abstract

fetched live from OpenAlex

Abstract: We use a survey approach to investigate the factors leading to the decision not to pay cash dividends in Canada. Our results show that Canadian managers perceive growth opportunities, low profitability and cash constraints as the major reasons underlying a firm's decision not to pay dividends. Questionnaire results also show that, for non‐dividend‐paying firms, taxation is at best a second‐order determinant of dividend policy and that stock repurchases are not substitutes for dividends. Finally, our findings are inconclusive regarding managers’ views on the relationship between dividend policy and stock prices and the signaling role of dividend policy.

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)0.003
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.533

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0140.005
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.030
GPT teacher head0.230
Teacher spread0.200 · 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

Citations30
Published2012
Admission routes1
Has abstractyes

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