The wages of social responsibility — where are they? A critical review of ESG investing
Bibliographic record
Abstract
Abstract This paper contributes both to investigating the link between the corporate social and financial performance based on environmental, social and corporate governance (ESG) ratings and to reviewing the existing empirical evidence pertaining to this relationship. The sample used includes ESG data of ASSET4, Bloomberg and KLD for the U.S. market from 1991 to 2012. The econometrical framework applies an ESG portfolio approach using the Carhart (1997) four‐factor model as well as cross‐sectional Fama and MacBeth (1973) regressions. Previous empirical research indicates a relationship between ESG ratings and returns. As against this, the ESG portfolios do not state a significant return difference between companies with high and low ESG ratings. Although the Fama and MacBeth (1973) regressions reveal a significant influence of several ESG variables, investors are hardly able to exploit this relationship. The magnitude and direction of the impact are substantially dependent on the rating provider, the company sample and the particular subperiod. The results suggest that investors should no longer expect abnormal returns by trading a difference portfolio of high and low rated firms with regard to ESG aspects.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".