A estrutura de eapital é eelevante para a rentabilidade dos bancos?: evidências empíricas nos maiores bancos brasileiros com papéis negociados na [B]3
Bibliographic record
Abstract
Objective: To identify evidence of the capital structure relevance and the main banking strategies for value maximization in banks.Method: This is an empirical-analytical, descriptive research with a quantitative approach. Longitudinal data from the Economatica® database were collected from the first quarter of 2008 to the fourth quarter of 2018. Linear regression tests were performed for the analyzes (panel data).Main results: There is evidence of the capital structure relevance and the influence of banking strategies on bank profitability measured based on ROICADJUSTED.Relevance and originality: The timeliness, relevance and inevitable critical debates on the subject arouse the interest of the academic area for research directed to the critical analysis of the capital structure relevance and operational strategies for value maximization.Theoretical Contributions: It provides the inevitable academic debate about capital structure relevance and key banking strategies for maximizing value in the largest publicly traded banks.Social Contributions: Contributes to the decision-making process of sector managers, investors, financial analysts and regulatory bodies by addressing the capital structure relevance to banks, the risks and costs involved, the key aspects that encourage high levels of financial leverage and the determining factors for profitability.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".