The impact of social responsibility disclosure and governance on financial analysts’ information environment
Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore the relationships between corporate social responsibility (CSR) disclosure, corporate governance and financial analysts’ information environment, as proxied by their ability to forecast a firm’s earnings. Hence, we extend prior voluntary disclosure research. Design/methodology/approach – Our paper considers that the determination of CSR disclosure, corporate governance and financial analyst forecasting work are closely intertwined. Therefore, we rely on simultaneous equations to explore these relations. Findings – Findings show that there is a direct relation between both CSR disclosure and corporate governance and financial analysts’ information environment: more disclosure and better governance translate into a tighter consensus in earnings forecasts as well as less dispersion. However, corporate governance substitutes for CSR disclosure in improving analyst forecast precision, thus supporting a comprehensive view of corporate governance that encompasses disclosure. Finally, results also suggest that CSR disclosure, through its effect on governance and analyst following, has an indirect influence on analyst forecast precision. Overall, it appears that both CSR disclosure and good corporate governance attract analysts and improve their ability to forecast earnings. Originality/value – To the best of our knowledge, our study is the first to investigate the joint effect of corporate governance and CSR disclosure on analyst forecast precision.
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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 | 0.009 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".