Market Reaction to Mandatory Nonfinancial Disclosure
Bibliographic record
Abstract
We examine the equity market reaction to events associated with the passage of a directive in the European Union (EU) mandating increased nonfinancial disclosure. These disclosures relate to firms’ environmental, social, and governance (ESG) performance, and would be applicable to firms listed on EU exchanges or with significant operations in the EU. We predict and find (i) an average negative market reaction of –0.79% across all firms, (ii) a less negative market reaction for firms having higher predirective nonfinancial performance, and (iii) a less negative reaction for firms having higher predirective nonfinancial disclosure levels. In addition, results are accentuated for firms having the most material ESG issues, as well as investors anticipating proprietary and political costs as a result of the mandated disclosures. Finally, we find that the negative market reaction is concentrated in firms with weak preregulation ESG performance and disclosure, which exhibit an average return of –1.54%; in contrast, firms with strong preregulation disclosure and performance exhibit an average positive return of 0.52%. Overall, the results are consistent with the equity market perceiving net costs (benefits) for firms with weak (strong) nonfinancial performance and disclosure around key events surrounding the mandatory disclosure regulation of nonfinancial information. This paper was accepted by Shiva Rajgopal, accounting.
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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 | 0.002 | 0.020 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".