The agency costs of investment opportunities and debt contracting: Evidence from exogenous shocks to government spending
Bibliographic record
Abstract
Abstract This study investigates the impact of macroeconomic shocks to firm investment opportunities on firm debt contracting policy. We find that adverse shocks to investment opportunities lead to a significant reduction in the use of debt covenants in syndicated bank loans. Consistent with incomplete contract theory, we show that firms mitigate debt–equity conflicts arising out of investment opportunities by employing accounting‐based financial covenants rather than non‐accounting‐based prepayment covenants. Adverse shocks to investment opportunities also lead to a concomitant decrease in the cost of borrowing. We find consistent evidence for corporate bond covenants and bond market borrowing costs as well. Overall, this study resolves prior mixed evidence concerning the impact of investment opportunities on debt contracting and connects macroeconomic theory with the accounting literature on debt contracting.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".