The Impact of Integrated Reporting on Analysts’ Forecasts
Bibliographic record
Abstract
Shareholders are very interested in the relationship between Integrated Reporting and analyst forecast accuracy. Integrated Reporting is deemed to reduce information asymmetry between the company and shareholders. The purpose of this paper is to provide evidence on the relationship between Integrated Reporting and analyst forecast accuracy. Analyst forecast accuracy is examined for a global sample of companies that adopted Integrated Reporting, companies that get assurance on Integrated Reporting, companies that receive assurance on their integrated reports by one of the Big 4, and for a south african sample, companies that are mandated to use Integrated Reporting. Information for analysts’ forecasts is retrieved from the I/B/E/S database and information for Integrated Reporting is retrieved from the GRI Sustainability Disclosure Database. We do not find a significant impact of Integrated Reporting on analyst forecast errors. Similarly, attestation of the reports by bigger or smaller audit firms does not seem to affect analysts’ forecast accuracy. In South Africa however, a positive impact on analysts’ forecast accuracy is observed suggesting that the effect of mandatory integrated disclosures is important for analysts’ forecasts.
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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 | 0.011 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".