The Informational Role of Bond Analysts
Bibliographic record
Abstract
ABSTRACT This study uses a large sample of sell‐side bond analysts' reports to examine the properties of recommendations provided by bond analysts and the impact of these recommendations on bond securities. First, we document that the distribution of bond analysts' buy, hold, and sell recommendations is skewed positively, but less so than the distribution of equity analysts' recommendations. The positive skewness in bond analysts' recommendations is greater for low than for high credit quality bonds. Second, we find that bond analysts' reports generate bond trading and return reactions that are both economically significant and greater for low credit quality bonds. The bond market reaction is greater for bond analysts' reports than for equity analysts' reports. Finally, while both bond and equity analysts lead rating agency announcements, we find no evidence of a difference in timeliness between bond and equity analysts' reports. Overall, our results are consistent with bond analysts issuing more negative reports than equity analysts and providing more information about low credit quality bonds as a result of the asymmetric demand for negative information by bond 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 | 0.004 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".