The Influence of Firm Maturation on Firms' Rate of Adjustment to Their Optimal Capital Structures
Bibliographic record
Abstract
Extant empirical research on firms' adjustment to their optimal capital structures is cross-sectional. However, Scholes and Wolfson (1989) argue that refinancing costs that accumulate with age increasingly impede firms from restoring their optimal capital structures. This study provides evidence on the time-series variation in the rate at which firms move toward their leverage targets that is consistent with this prediction. In separate tests, age is measured from two dates—from firms' initial public offerings and from their incorporation—to examine whether the duration of their public and private experience, respectively, affect the evolution in financial policies. This paper contributes to the literature by developing a research design that isolates the influence of dynamic refinancing costs on the leverage adjustment problem. The evidence also justifies future research on Scholes and Wolfson's (1989) predictions about the time-series pattern in firms' tax shields by empirically validating that refinancing costs increasingly constrain their capital structures.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".