The impact of ESG performance on firm-idiosyncratic risk in the US and Canada
Bibliographic record
Abstract
This paper aims to examine how environmental, social, and governance (ESG) performance affects the idiosyncratic risk of firms in the United States and Canada between 2007 and 2020. This study retrieves ESG scores and other factors from the Refinitiv DataStream, the firms used for analysis in this paper are 480 listed companies.�Results show that ESG performance can reduce idiosyncratic risk in different firms' characteristics and periods. First, ESG performance can subdue the idiosyncratic risk in both sensitive and non-sensitive industries at the same level. Second, only the environmental pillar in the sensitive industry has an additional negative influence on idiosyncratic risk due to the concentration in environmentally sensitive industries of the samples. Third, high market value firms tend to benefit more from improving ESG performance than low market value firms. Fourth, the effect size of ESG performance on idiosyncratic risk of low leverage firms is larger than high leverage firms. Fifth, ESG practice shows a more considerable effect in times of recession periods compared to normal periods. Lastly, ESG performance can subdue idiosyncratic risk of firms in covid-19 period higher than in pre-covid-19 periods.�
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".