MétaCan
Menu
Back to cohort
Record W3124467207

Why Individual Investors want Dividends

2003· preprint· en· W3124467207 on OpenAlexaff
Ming Dong, Chris Robinson, Chris Veld

Bibliographic record

VenueResearch portal (Tilburg University) · 2003
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsSimon Fraser UniversityYork University
Fundersnot available
KeywordsDividendAgency costDividend policyCashFinancial economicsEconomicsStock (firearms)Behavioral economicsBusinessMonetary economicsFinanceShareholder
DOInot available

Abstract

fetched live from OpenAlex

The question of why individual investors want dividends is investigated by submitting a questionnaire to a Dutch investor panel. The respondents indicate that they want dividends partly because the cost of cashing in dividends is lower than the cost of selling shares. Their answers provide strong confirmation for the signaling theories of Bhattacharya (1979) and Miller and Rock (1985). They are inconsistent with the uncertainty resolution theory of Gordon (1961, 1962) and the agency theories of Jensen (1986) and Easterbrook (1984). The behavioral finance theory of Shefrin and Statman (1984) is not confirmed for cash dividends but is confirmed for stock dividends. Finally, our results indicate that individual investors do not tend to consume a large part of their dividends. This raises some doubt as to whether a reduction or elimination of dividend taxes will stimulate the economy.

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)0.008
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.101
GPT teacher head0.278
Teacher spread0.178 · 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

Citations3
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueResearch portal (Tilburg University)Same topicCorporate Finance and GovernanceFrench-language works237,207