Assessing Rating Agencies' Ability to Predict Bank Bankruptcy – The Lace Financial Case
Bibliographic record
Abstract
Abstract This paper responds to renewed interest in the following controversial question: do rating agencies have the ability to predict the risk of bank bankruptcy in a timely manner, and are they able to communicate it on time to the banking system? We tried to provide an answer to this question by checking when US banks that failed in 2009 were downgraded to Non‐Investment Grade (E). The database for this analysis consists of 116 US banks failing in 2009. The rating agency considered is Lace Financial Corporation. The study analyses the time series of ratings for the sample banks from the fourth quarter of 2005 to the date of bankruptcy and shows that over 72 per cent of the US banks that failed in 2009 had been downgraded to E in the fourth quarter prior to failure and 94 per cent had been rated E six months prior to bankruptcy. Empirical evidence from the Lace case does support the view that the Credit Rating Agency provides timely information to market participants .
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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 | 0.023 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".