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Record W4415105086 · doi:10.1080/09638180.2025.2564794

Trust, Corporate Social Responsibility, and the Market Pricing of Corporate Earnings

2025· article· en· W4415105086 on OpenAlexaff
Eli Bartov, Yan Li

Bibliographic record

VenueEuropean Accounting Review · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsTrent University
Fundersnot available
KeywordsEarningsCorporate governanceCorporate social responsibilityEarnings managementCorporate financeCorporate security

Abstract

fetched live from OpenAlex

Investor trust is central to the functioning of capital markets, yet its relationship with corporate social responsibility (CSR) remains underexplored. We investigate whether CSR performance affects the market’s reaction to earnings news. Our results show that firms with superior CSR performance exhibit stronger immediate market reactions to earnings surprises and significantly smaller post-earnings-announcement drift. We provide evidence that this effect arises from CSR enhancing investor trust, thereby reducing uncertainty and attenuating investors’ systematic underreaction to earnings information. Our findings hold even after controlling for financial reporting quality and corporate governance, establishing that the CSR effect is distinct. This study highlights that CSR performance enhances investor trust, ultimately leading to more efficient pricing of earnings information.

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)0.002
metaresearch head score (Gemma)0.017
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 score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.019
GPT teacher head0.231
Teacher spread0.212 · 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 routes1
Has abstractyes

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