When Do Qualitative Risk Disclosures Backfire? The Effects of a Mismatch in Hedge Disclosure Formats on Investors' Judgments
Bibliographic record
Abstract
ABSTRACT Disclosure standards mandate the quantitative disclosure of hedging‐instrument‐related risks but not the disclosure of hedged‐item‐related risks. We examine how a match (mismatch) in formats, caused by making quantitative (qualitative) hedged item disclosures alongside quantitative hedging instrument disclosures, affects investors' integration of information from these two related disclosures. Our first experiment varies the hedged item disclosure format (quantitative or qualitative) and the portion of risk hedged (small or large). We find that when disclosure formats are mismatched, the less comparable nature of the two disclosures caused investors to neglect the offsetting relationship when assessing net risks. As a result, risk and investment judgments were influenced by the more prominent quantitative hedging instrument disclosures. Our second experiment finds that the use of a qualitative debiaser that clarifies the relationship between the two disclosures led to the integration of information and mitigated this effect.
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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 | 0.015 | 0.137 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".