Systematic ESG exposure and stock returns: Evidence from the United States during the 1991–2019 period
Bibliographic record
Abstract
Abstract Using a sample of US stocks over the period 1991–2019, we test whether stocks with high exposure to a social index exhibit high returns. Using a univariate analysis, our in‐sample results show that stocks with high sensitivities to the MSCI KLD 400 Social Index underperform stocks with low sensitivities by an annual risk‐adjusted performance of 7.02%. The negative premium is also larger in the post‐crisis period of 2007–2019 and is equal to 10.25%. The out‐of‐sample results offer, however, only weak evidence of such a finding, with a risk‐adjusted performance difference of merely −0.84% over the full sample period and no significant differences between the pre‐crisis and post‐crisis periods. In the multivariate regression, we find evidence of a negative relationship between exposure to the social index and stock performance. Moreover, we find that stocks with high exposure to the social index display a low corporate social responsibility score, a high Tobin’s Q , high long‐term debt, a large size, high total risk, a high market beta, a high SMB coefficient, a low HML coefficient, and a small MOM coefficient.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".