MétaCan
Menu
Back to cohort
Record W3121408795 · doi:10.1287/mnsc.2016.2551

Do Investors Value Dividend-Smoothing Stocks Differently?

2016· article· en· W3121408795 on OpenAlexaff
Yelena Larkin, Mark T. Leary, Roni Michaely

Bibliographic record

VenueManagement Science · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsYork University
Fundersnot available
KeywordsDividendDividend policyMonetary economicsStock (firearms)Financial economicsSmoothingEnterprise valueEconomicsBusinessShareholderFinanceCorporate governance

Abstract

fetched live from OpenAlex

It is widely documented that managers strive to maintain smooth dividends. Yet, it is not clear if this behavior reflects investors’ preferences. In this paper, we study whether investors indeed value dividend-smoothing stocks differently by exploring the implications of dividend smoothing for firms’ investor clientele, stock prices, and cost of capital. We find that retail investors are less likely to hold dividend-smoothing stocks, while institutional investors, and especially mutual funds, are more likely. However, this preference does not result in any detectable relation between the smoothness of a firm’s dividends and the expected return, or market value, of its stock. Together, the evidence suggests that firms adjust the supply of smoothed dividends to match investors’ demand. Dividend smoothing affects the composition of a firm’s shareholders but has little impact on its stock price. This paper was accepted by Amit Seru, finance.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.000
Research integrity0.0010.001
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 head0.022
GPT teacher head0.222
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

Citations71
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueManagement ScienceSame topicCorporate Finance and GovernanceFrench-language works237,207