The impact of social responsibility disclosure and governance on financial analysts’ information environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 |
| 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 it