Lead bank quality and adverse rating announcements
Bibliographic record
Abstract
Purpose This paper seeks to examine whether the market values the monitoring activity undertaken by a quality bank in the presence of a credit rating agency. Specifically, the question is asked whether the quality of a lead lending bank influences a market reaction to adverse rating announcements concerning its borrowers. Design/methodology/approach The event study methodology and various bank quality proxies (size, growth rate in assets, profitability, capital ratio, bank's credit rating, and ownership) are used to examine the market reaction when a borrower's bank loan rating is placed with negative implication or is downgraded. Findings Firms which are certified and monitored by high‐quality banks are less susceptible to negative market reactions when adverse rating announcements are made. Originality/value The findings indicate high‐quality lending banks sustain investors' confidence in their borrowers in the face of deteriorating news. The paper argues that investors and borrowers value monitoring from a high‐quality bank, which is an implication of a bank having access to private information about its borrowers.
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 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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".