Determinants of Corporate Capital Structure: A Theoretical Integration and Some Empirical Evidences
Bibliographic record
Abstract
This research reviews all of the relevant important theories and concepts developed in corporate capital structure until till date in an aggregate manner. The empirical part of the study reveals that the leverage ratios defined in short-term debts, long-term debts, total debts and book value of assets are correlated. Similarly, the leverage ratios defined in short-term debts, long-term debts, total debts and market value of assets are correlated. However, book value based and market value based leverage ratios are not correlated. The leverage ratios defined in earnings before interest and taxes over interest and earnings before interest, taxes and depreciation over interest are positively perfectly correlated. Besides, short-term loans are three times more compare to long term debts, firms are reluctant in paying tax and allotment in research and development expenses are insufficient. In addition, industry median average, non-debts tax shield, uniqueness (R&D) positively significantly affects financial leverage and, and size, tangibility, tax rate, dividend pay-out, agency cost, business risk, GDP growth, and money growth negatively significantly affects financial leverage. The selling, general and administrative expenses positively affects short-term debts, negatively affects long-term debts and have no significant effects on total debts. Last but not least, human capital cost do not have affect on any kind of leverage.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".