Agency cost of debt overhang with optimal investment timing and size
Bibliographic record
Abstract
Abstract The concept of debt overhang (that is, an equity‐maximizing levered firm will under‐invest relative to a firm‐value‐maximizing firm) is well established in the literature. A number of papers have demonstrated it as delayed investment (when investment size is specified) or smaller investment (when investment time is specified). However, there is no work on the underinvestment effect when the firm chooses both size and timing of investment, as it usually does in real life. This is what our paper focuses on. When the firm has the flexibility to choose both size and time, the effect is complicated by the fact that delayed investment results in larger investment, which suggests that the underinvestment problem might be mitigated. We find, however, that the effect depends on how underinvestment is measured. When measured by the expected present value of investment, flexibility can mitigate or exacerbate the underinvestment problem, depending on the cost of installing capacity. But when measured by the agency cost, flexibility always exacerbates the underinvestment problem. It is shown numerically that, at the optimal leverage ratio, the agency cost with plausible parameter values can be economically significant. Thus, with the flexibility of choosing both time and size of investment, the debt overhang problem can be of significant practical relevance in corporate investment decisions.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".