Determinants of the Capital Structure of Companies Listed on the Stock Exchanges of Argentina, Brazil and Chile: An Empirical Analysis of the Period from 2007 to 2016
Bibliographic record
Abstract
The present investigation refers to the determinants of the capital structure, using the technique of multiple regression through Panel Data of open capital companies in the stock exchanges of Argentina, Brazil and Chile, in order to know the behavior of determinants of the capital structure in relation to Trade-Off Theory (TOT) and Pecking Order Theory (POT). The POT offers the existence of a hierarchy in the use of sources of resources, while the TOT considers the existence of a target capital structure that would be pursued by the company. Sixteen accounting variables were used, in which five are dependent (related to indebtedness) and eleven are independent variables (explaining the determinants of the capital structure). It is observed that, with the use of the Panel Data, the determinants that seem to influence in a more accentuated way the levels of debt of the companies are: current liquidity, tangibility, return to shareholders, return of assets, sales growth, asset growth, market-to-book and business risk measured by the volatility of benefits. Suggestions for future research include the use of Panel Data to analyze other factors that may influence indebtedness, mainly taxes and dividends, as well as a deeper analysis of factors that may influence the speed of adjustment towards the supposed objective level.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".