Audited financial reporting and voluntary disclosure: International evidence on management earnings forecasts
Bibliographic record
Abstract
In this paper, we extend prior research on the link between audited financial reporting and voluntary disclosure by examining international differences in the relationship between commitment to higher levels of audit verification of actual financial outcomes and management earnings forecasts (our proxy for voluntary disclosure), using firm‐level data from 30 non‐US countries. Our evidence that commitment to higher levels of audit verification (proxied by the choice of a Big 4 auditor, the amount of audit fees, and excess audit fees) is positively associated with the incidence and frequency of management forecasts, and with stock market reactions to such forecasts, supports the notion that audited financial reporting and voluntary disclosure of managers' private information are complements in countries around the world. We further find that the relation between audited financial reporting and management earnings forecasts is weaker for firms in countries with relatively stronger capital market development or with higher levels of investor protection, suggesting that audited financial reporting plays a more important complementary role in voluntary disclosure in countries with less‐developed institutions. Overall, our findings suggest that firm‐level commitment to better audited financial reporting and the strength of country‐level institutional characteristics play substitute roles in corporate voluntary disclosure decisions.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".