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Record W4210630416 · doi:10.22495/cocv19i2art4

The frequency of say-on-pay vote, shareholder value, and corporate governance

2022· article· en· W4210630416 on OpenAlex
Justin Yiqiang Jin, Na Li

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCorporate Ownership and Control · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsYork UniversityMcMaster University
Fundersnot available
KeywordsShareholderCorporate governanceExecutive compensationBusinessEquity (law)AccountingStock marketMarket valueFinance

Abstract

fetched live from OpenAlex

Using a sample of 1,079 public firms listed on the U.S. stock market that filed the results of their frequency votes in 2011, we examine the market reaction to shareholders’ decision on the frequency of the say-on-pay vote, and the relation between such decision and firms’ existing corporate governance structures. When firms release the results of their shareholders’ frequency vote in Form 8-K, we find that market reaction was significantly positive for firms with excess CEO equity pay, and for firms whose shareholders’ preference for the frequency is the same as that recommended by the board. This positive market reaction is more pronounced for firms where shareholders change the recommendations of the boards by demanding more frequent votes on executive compensation. Overall, our study on the frequency of votes provides new insights that are different from prior studies, which mostly focus on say-on-pay votes. We show that the market perceives the shareholders’ frequency vote as a value-increasing governance mechanism and a complement to the existing corporate governance

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.377
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.028
GPT teacher head0.194
Teacher spread0.167 · 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