Decisões de Estrutura de Capital das Empresas Brasileiras de Capital Aberto a Partir das Teorias de Pecking Order e Trade-off e a Influência da COVID-19.
Bibliographic record
Abstract
Objective: The present study aimed to evaluate the capital structure decisions of Brazilian publicly traded companies based on the theories of Pecking Order and Trade-off and the influence of COVID-19. Background: supported by the theories of Pecking Order and Trade-off, antagonistic hypotheses were established to ascertain which theory best explains the capital structure decisions in the period analyzed and whether there was influence of COVID-19. Method: regression was performed with panel data, fixed and balanced model, from three models, for 187 B3 companies, whose financial statements, consolidated and adjusted for inflation, were available in Economática®. The data, quarterly, corresponded to the period from the 2nd quarter of 2018 to the 4th quarter of 2021. A dummy variable was included in the model to identify the pre-pandemic (2018 and 2019) and post-pandemic (2020 and 2021) periods. Results: the results indicate that companies increased the level of indebtedness in the post-pandemic period, however, the pandemic variable was not significant in any model. The profitability variable was significant and positive in relation to long-term indebtedness, following the assumptions of the Trade-off. However, it was significant and negative with respect to short-term indebtedness, as established in the Pecking Order. The growth opportunity, on the other hand, showed a significant and positive relationship, also in accordance with the Pecking Order. Contributions: this study contributed to identify that the concepts established by the Pecking Order were more present in the capital structure decisions of Brazilian publicly traded companies in the period analyzed.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".