Bayesian Persuasion in Credit Ratings, the Credit Cycle, and the Riskiness of Structured Debt
Bibliographic record
Abstract
We present a new theoretical model that sheds light on why CDO tranche spreads widen during credit crunch periods. In the model, firms’ risk taking is endogenous and credit ratings arise from an investigation process that is designed to maximize the proportion of firms with high ratings (Bayesian persuasion). We show that the rating agency changes rating standards over the business cycle. If the economy enters a recession, the deteriorating quality of fundamentals implies that debt issued in booms may not be incentive compatible with low-risk behavior. In this case, the rating agency undertakes a more stringent rating investigation to increase the precision of ratings and hence to reduce the cost of capital with good ratings. Highly rated firms can only realize the benefits of more precise ratings by calling existing debt and issuing lower cost debt. This may not be possible during a credit crunch, and hence the resulting high risk strategy by firms in such periods implies that senior tranches, which are nearly riskless at the time of issuance, get seriously impacted. We find support for this hypothesis in the data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".