MétaCan
Menu
Back to cohort
Record W1625098121

The Dividend and Share Repurchase Policies of Canadian Firms

2001· article· en· W1625098121 on OpenAlexaboutno aff
Abe de Jong, Ronald van Dijk, Chris Veld

Bibliographic record

VenueERIM Report Series Research in Management · 2001
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Management and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsShare repurchaseDividendFree cash flowBusinessShareholderDividend policyStock exchangeDividend payout ratioStock (firearms)CashCash flowMonetary economicsFinanceEconomicsCorporate governance
DOInot available

Abstract

fetched live from OpenAlex

We empirically investigate dividend and share repurchase policies of Canadian firms. We have sent a questionnaire to the 500 largest non-financial Canadian companies listed on the Toronto Stock Exchange, of which 191 usable responses were returned. These data are used to measure firm characteristics. We use several logit regression analyses to test the structure and determinants of the dividend and share repurchase choice. Our results are consistent with a structure in which the company first decides whether it wants to pay out cash to its shareholders or not. In the second stage the firm decides on the form of the payout: dividends, share repurchases or both. Payout is determined by free cash flow. The choice for dividends and repurchases depends on behavioral and tax preferences. Furthermore, the payout is less likely to be dividends if the company has executive stock option plans. Finally, we find evidence for the Brennan and Thakor (1990) model. According to this model the existence of asymmetric information amongst outsiders is associated with a preference for dividend payments over share repurchases.

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)0.007
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 score0.047
Threshold uncertainty score0.339

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.113
GPT teacher head0.323
Teacher spread0.210 · 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

Citations1
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueERIM Report Series Research in ManagementSame topicOrganizational Management and LeadershipFrench-language works237,207