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Record W3171516297 · doi:10.1111/1911-3846.12705

Investors' Perceptions of Activism via Voting: Evidence from Contentious Shareholder Meetings*

2021· article· en· W3171516297 on OpenAlexvenueno aff
François Brochet, Fabrizio Ferri, Gregory S. Miller

Bibliographic record

VenueContemporary Accounting Research · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsShareholderVotingProxy votingProxy (statistics)Stock (firearms)BallotInstitutional investorBusinessAccountingMonetary economicsCorporate governanceEconomicsPolitical scienceFinanceDisapproval votingLawGeography

Abstract

fetched live from OpenAlex

ABSTRACT Motivated by the increasing influence of shareholder votes on corporate policies, we examine investors' perceptions of activism via voting. To identify instances of activism via voting, we focus on annual meetings with at least one ballot item where a substantial fraction of shareholders is expected to vote against management's voting recommendation, indicating an increase in their monitoring activity. We define such meetings as “contentious.” Using a sample of almost 28,000 meetings between 2003 and 2012, we examine stock returns over the period between the proxy filing and the annual meeting. This period captures when investors learn about the contentious nature of the upcoming meeting and form expectations about its likely impact on firms' policies. We find that abnormal stock returns prior to contentious meetings are significantly positive and higher than those prior to noncontentious meetings. These higher abnormal returns increase with the contentiousness of the meeting; are more pronounced in firms with poor past performance, which are more likely to respond to shareholder pressure; and persist after controlling for firm‐specific news and proxies for risk factors. Our results are consistent with investors' expecting activism via voting to have a positive impact on firm value, on average, and cast doubts on regulatory attempts to restrict the use of shareholder votes.

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 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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.133
GPT teacher head0.320
Teacher spread0.187 · 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 teacher head, not a consensus.

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

Citations44
Published2021
Admission routes1
Has abstractyes

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