The impact of environmental, social and governance (ESG) scores on stock market: evidence from G7 countries
Bibliographic record
Abstract
Purpose This study aims to examine the effect of environmental, social and corporate governance (ESG) scores on stock markets for the period from February 2018 to December 2022 for G7 countries. Even though ESG is an established area of investigation, prior research has paid inadequate attention to the nexus of ESG scores and stock markets in G7 (Germany, USA, UK, Italy, France, Japan and Canada) countries. Design/methodology/approach This study covers G7 countries and uses a data set, which includes ESG scores and stock market returns from reporting channels including financial websites, and international indexes, between February 2018 and December 2022. Cross-section dependency and homogeneity tests were used with Konya (2006) panel causality test to investigate the relations of ESG scores and stock markets, and the research also conducted a separate analysis for each sub-dimension. Homogeneity/heterogeneity tests were also carried out in the research. Findings The findings suggest that causality from ESG scores to stock market (DAX) was determined only for Germany. Accordingly, it is understood that German companies have started to implement corporate social responsibility and ESG practices in their management strategies and reporting. These findings offer important implications for those who are considering investing in G7 countries, whether or not to consider ESG scores. Originality/value In this context, the research contributes to the existing literature on the relationships between ESG scores and stock markets, which are seen as a vital tool to meet the expectations of stakeholders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".