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Record W2593198516

Environmental, Social and Corporate Governance Factors and the Investor's Efficient Frontier

2016· article· en· W2593198516 on OpenAlexfundno aff
An H. Pham, Chen Zhang

Bibliographic record

VenueSummit (Simon Fraser University) · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
FundersSimon Fraser University
KeywordsCorporate governanceFrontierBusinessFinancePolitical science
DOInot available

Abstract

fetched live from OpenAlex

Socially responsible investing (SRI) has been a common practice since the late 1980s. At the beginning, academics, practitioners and the public have asked if the ESG integration process hurt investment returns. The answer to this question depends on the future growth of ESG screening process and rating data. If research shows that there is no significant difference in returns between a portfolio without ESG constraint and a portfolio with ESG constraint, SRI will be appealing to investors who allow peace of mind knowing that they are invested in socially responsible companies. If it is shown that ESG portfolios produce superior returns, SRI will become mainstream and traditional investors will begin integrating ESG factor. Our research paper suggests that ESG investors are not efficient or rational investors based on the modern portfolio theory, and there is no evidence confirming that the benefit of ESG integration outweighs the loss of portfolio efficiency.

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)0.004
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0040.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.186
Teacher spread0.166 · 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
Published2016
Admission routes1
Has abstractyes

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