Fundamental Risk and Capital Structure Adjustment Speed: International Evidence
Bibliographic record
Abstract
This study investigates the impact of countries’ fundamental risk on the speed of adjustment (SOA) towards firms’ target capital structures. Using a dataset comprising 17,747 non-financial firms from 44 countries, this study finds that a reduction in country-specific fundamental risk significantly increases a firm’s rate of leverage adjustment. More specifically, we observe that a one standard deviation reduction in fundamental risk results in a substantial 12.79% increase in SOA for book leverage and a 4.81% increase for market leverage. The study also finds evidence of the influence of individual dimensions of fundamental risk on SOA. It implies that improved operational efficiency, high foreign accessibility, enhanced corporate transparency, and increased political stability expedite the pace of leverage adjustment within firms. Robustness checks using a machine learning random forest estimator predicted leverage targets to corroborate these findings. The results highlight the critical role of institutional quality in reducing financing frictions and promoting more efficient corporate capital adjustments. These insights have profound implications for policymakers, emphasising the need to strengthen institutional and regulatory frameworks to enhance capital market integrity and reduce friction, which could ultimately create value for the firm stakeholders.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".