Capital structure and sector competitiveness in companies listed on B3 during th COVID-19 crisis
Bibliographic record
Abstract
The objective of this work is to evaluate the influence of the COVID-19 health crisis and the market competition of different sectors on the capital structure of Brazilian companies. Supported by trade-off and pecking order theories, a sample of Brazilian companies listed on B3 was used, referring to the 2nd quarter of 2018 to the 1st quarter of 2022. The determinant variables of the capital structure used were: profitability, size, tangibility, asset composition, growth, the competitiveness proxy, was measured by the Herfindahl-Hirschman index and the COVID-19 health Crisis Dummy. The method employed, due to the presence of heteroscedasticity and autocorrelation, was the Feasible Generalized Least Squares regression. The results supported the dynamics of indebtedness against the pecking order, where higher returns tend to need less external funding and the trade-off, showing that the relationship with greater tangibility and larger firms generally find it easier to obtain financing. Regarding the COVID-19 crisis, it is concluded that it had a positive influence analyzed by the market sector. The research contributes to a better understanding of capital structures in the face of the trade-off and pecking order and the COVID-19 health crisis
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".