The Effect of Risk Factor Disclosures on the Pricing of Credit Default Swaps
Bibliographic record
Abstract
ABSTRACT This study examines the relation between narrative risk disclosures in mandatory reports and the pricing of credit risk. In particular, we investigate whether and how the Securities and Exchange Commission (SEC) mandate of risk factor disclosures (RFDs) affects credit default swap (CDS) spreads. Based on the theory of Duffie and Lando (2001), we predict and find that CDS spreads decrease significantly after RFDs are made available in 10‐K/10‐Q filings. These results suggest that RFDs improve information transparency about the firm's underlying risk, thereby reducing the information risk premium in CDS spreads. The content analysis further reveals that disclosures pertinent to financial and idiosyncratic risk are especially relevant to credit investors. In cross‐sectional analyses, we document that RFDs are more useful for evaluating the business prospects and default risk of firms with greater information uncertainty/asymmetry. Overall, our findings imply that the SEC requirement for adding a risk factor section to periodic reports enhances the transparency of firm risk and facilitates credit investors in evaluating the credit quality of the firm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.074 |
| 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 | 0.001 |
| 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".