Attributes of Corporate Risk Disclosure: An International Investigation in the Manufacturing Sector
Bibliographic record
Abstract
ABSTRACT This paper is the first multi-country investigation of comprehensive corporate risk disclosure. Based on a detailed content analysis of 160 annual reports, we analyze the attributes and the quantity of risk disclosure and its association with the level of firm risk in the U.S., Canadian, U.K., and German settings. We find a consistent pattern where risk disclosure is most prevalent in management reports, concentrates on financial risk categories, and comprises little quantitative and forward-looking disclosure across sample countries. In terms of risk disclosure quantity, U.S. firms generally dominate, followed by German firms. Cross-country variation in risk disclosure attributes can only partly be linked to domestic disclosure regulation, suggesting that risk disclosure incentives play an important role. While risk disclosure quantity appears to be positively associated with proxies of firm risk in the North American settings, we find a negative association with leverage for Germany. This coincides with a “concealing motive” implied by an insider role of banks in the German financial setting.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".