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Record W3121720159

Proxy Advisors As Issue Spotters

2020· article· en· W3121720159 on OpenAlexaff
Douglas Sarro

Bibliographic record

VenueSSRN Electronic Journal · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Governance and Law
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProxy (statistics)Proxy votingVotingShareholderInstitutional investorBusinessCommissionAccountingVoting trustLaw and economicsPublic relationsEconomicsFinancePolitical scienceCorporate governanceDisapproval votingLawComputer science
DOInot available

Abstract

fetched live from OpenAlex

When institutional investors hire proxy advisors to prepare reports on matters up for vote at public company shareholder meetings, are they interested primarily in acquiring a bottom-line recommendation on how to vote, on which they can then blindly rely? Or in acquiring information that will help them make their own voting decisions? Supporters of controversial reforms introduced by the Securities and Exchange Commission (SEC) in 2019 and 2020 gravitate toward the former position, arguing that reform is needed to discourage undue reliance on proxy advisor recommendations. Opponents gravitate toward the latter position, arguing that additional regulation generally is unnecessary given that institutional investors already review their proxy advisors’ work product and make their own voting decisions. This article argues that neither of these positions presents a full picture of proxy advisors’ role in shareholder voting, and puts forward a more nuanced account that better reflects existing empirical evidence: institutional investors tend to use proxy advisors first and foremost as issue spotters, helping them distinguish (i) controversial matters that require a review of the proxy advisor’s analysis and potentially other information sources from(ii) non-controversial matters where they can vote in line with the proxy advisor’s recommendation without undertaking further review. On this account, proxy advisors do influence shareholder voting, but this influence derives primarily from their ability to direct institutional investors’ attention away from some proposals and toward others, rather than from institutional investors’ following their recommendations in lockstep. This account casts one common criticism of proxy advisors’ standards—that they reflect a one-size-fits-all approach to corporate governance that results in recommendations that do not reflect each public company’s unique circumstances—in a new light that exposes potential problems unaddressed by the SEC’s reforms. At the same time, it casts doubt on the usefulness of many of the reforms introduced by the SEC, which appear to be predicated on the flawed assumption that blind reliance on proxy advisor recommendations is a serious problem.

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)0.009
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.047
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.003
Scholarly communication0.0080.007
Open science0.0010.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0200.005

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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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 designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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