Is the Tone of Risk Disclosures in MD&As Relevant to Debt Markets? Evidence from the Pricing of Credit Default Swaps<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT This paper examines whether the tone of corporate textual disclosures related to risk and uncertainty conveys relevant information to the credit default swap (CDS) market. Prior studies largely focus on the amount of risk disclosures and provide inconclusive evidence on the usefulness of risk disclosures for investors in assessing firm risk. Using a large sample of textual risk disclosures in the Management's Discussion and Analysis (MD&A) section of 10‐K and 10‐Q filings, I predict and find that the change in CDS spreads over the three‐day window surrounding the 10‐K/Q filing date is positively associated with the pessimism of the language used in the risk disclosures. I conduct several analyses to show that the effect of the tone of risk disclosures is distinguishable from that of the amount of such disclosures. Cross‐sectional analyses reveal that the CDS market reaction to the tone of MD&A risk disclosures is more pronounced for reference entities closer to default, consistent with creditors' particular concern about downside risk. Further, the CDS market reacts more significantly to the tone of MD&A risk disclosures for reference entities with a weaker information environment. Overall, these results support the view that the tone of textual risk disclosures in MD&As has information content for investors in the CDS market in particular and debt markets in general. My findings improve the understanding of textual risk disclosures by showing that the tone and the amount of such disclosures have different implications for debt market investors' risk perceptions.
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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 | 0.038 |
| 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 | 0.001 |
| 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".