Institutional Investor Attention, Agency Conflicts, and the Cost of Debt
Bibliographic record
Abstract
Using a new measure of shareholder inattention constructed from exogenous industry shocks to institutional investor portfolios, we find that firms with distracted shareholders are associated with a higher cost of debt. This effect is stronger for firms with more powerful CEOs, firms with higher information asymmetry, and those operating in less competitive product markets. Further testing suggests that the inattention-cost of debt relation is driven primarily by dual holders directly observing shareholder distraction. Our results are robust to controlling for inattention at the retail investor level and to other external monitors, including credit rating agencies, financial analysts, and Big 4 auditors. Overall, our evidence suggests that institutional shareholder inattention has an incrementally negative effect on bond pricing. This paper was accepted by Brian Bushee, accounting. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4593 .
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".