Does credit default swap trading improve managerial learning from outsiders?
Bibliographic record
Abstract
Abstract We investigate whether credit default swap (CDS) trading results in managers learning new information through stock prices that is relevant to their investment and forecasting decisions. We argue that the CDS market structure, the sophistication of CDS market participants, and the cleanness of CDS spreads as a signal of default risk together produce and convey information that is new to managers of firms referenced in CDS contracts. We consider two measures for managerial learning: (1) the sensitivity of managerial investments to share prices and (2) the sensitivity of changes in management forecast accuracy to stock returns. We find that both sensitivity measures increase significantly when firms are referenced in any traded CDS contracts, indicating that CDS trading improves managerial learning. We also find that the improvement in managerial learning is more pronounced for firms that are subject to higher uncertainty in industry‐specific and economy‐wide prospects, consistent with the view that CDS market participants have informational advantages with respect to the industry‐level and macroeconomic environments. We further find that the improvement in managerial learning is more evident for firms with higher credit risk. Our findings provide large‐sample evidence on a positive consequence of CDS trading in the context of managers' ability to learn from outside investors.
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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 | 0.021 |
| 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 | 0.001 | 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".