Debt Analysts' Views of Debt-Equity Conflicts of Interest
Bibliographic record
Abstract
ABSTRACT We investigate how the tone of sell-side debt analysts' discussions about debt-equity conflict events affects the informativeness of debt analysts' reports in debt markets. Conflict events such as mergers and acquisitions, debt issuance, share repurchases, or dividend payments potentially generate asset substitution or wealth expropriation by equity holders. We document that debt analysts routinely discuss these conflict events in their reports. More importantly, discussions about conflict events that we code as negative are associated with increases in credit spreads and bond trading volume. Consistent with the informational value of debt analysts' discussions in secondary debt markets, we find that negatively coded conflict discussions predict higher bond offering yields in the primary bond market. In additional analyses, we measure the tone of debt analysts' discussions based on their disagreement with the tone of equity analysts' discussions and find that the informativeness of debt analysts' reports is higher when our coding indicates that conflict events are viewed negatively by debt analysts but positively by equity analysts. JEL Classifications: G12, G14, G32, M49.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".