Bibliographic record
Abstract
In this study, we investigate the relationship between environmental, social, and governance (ESG) disclosures and stock price crash risk. A stock price crash is a dreadful event for market participants. Thus, exploring stock price crash determinants is helpful for investment decisions and risk management. In this study, we use samples of major market index components in Europe, the United States, and Japan to perform regression analyses, after controlling for other potential stock price crash determinants. We estimate static two-way fixed-effect models and dynamic GMM models. We find that coefficients of firm-level ESG disclosures are not statistically significant in the static model. ESG disclosure coefficients in the dynamic model are not statistically significant in the U.S. market sample. On the other hand, coefficients of ESG disclosure scores in the dynamic model are statistically significant and negative in the European and Japanese marker sample. Our findings suggest that ESG disclosures lower future stock price crash risk; however, the effect and predictive power of ESG disclosures differ among regions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".