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Record W4413388474 · doi:10.1016/j.irfa.2025.104573

Value effects of sovereign wealth funds' exclusionary policies: The case of the Norwegian government pension fund-global (NGPF-G)

2025· article· en· W4413388474 on OpenAlexafffund
Isaac Otchere, Hanh Thi My Phan

Bibliographic record

VenueInternational Review of Financial Analysis · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicState Capitalism and Financial Governance
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsSovereign wealth fundNorwegianPensionPension fundValue (mathematics)EconomicsFinancial systemGovernment (linguistics)BusinessMonetary economicsFinanceMacroeconomics

Abstract

fetched live from OpenAlex

We examine the value effects of exclusions by the NGPF-G, the world's largest ethical sovereign wealth fund and find that the exclusions elicited mixed response from investors in the short term. The excluded firm experienced significantly negative returns on the announcement date and the week following the exclusions. However, the firms realized higher returns over the three weeks after the exclusion compared to a control sample of firms that are still in the portfolio of the NGPF-G. Analysis of the institutional holdings shows that hedge funds increased their investments in the excluded firms. The excluded firms' operating performance significantly improved after the exclusion. However, consistent with the assertion that the shares of the excluded firms could be overvalued in the short term because the market is unable to price the environmental and social risk which culminated in the exclusion, we find that the excluded firms' underperformed the control sample four years after the exclusion.

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)0.003
metaresearch head score (Gemma)0.010
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 score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.002
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 head0.008
GPT teacher head0.262
Teacher spread0.254 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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