Do Capital Buffers Matter? A Study on the Profitability and Funding Costs Determinants of the Brazilian Banking System
Bibliographic record
Abstract
This paper consists of an empirical investigation of Brazilian banks' profitability determinants. The panel data is composed of quarterly information for 71 banks between the first quarter of 2002 and the second quarter of 2012. Using data from the Brazilian banking system, we study the traditional determinants of bank profitability - controlling for macroeconomic environment, bank-specific characteristics and industrial structure of the banking sector - and contribute by analyzing the effects of capital buffers on bank profitability. We find that capital buffers have a positive impact on Brazilian banks' profitability. This result reinforces the hypothesis that buffers signalize stability and safety, reducing costs of fund raising. Other findings include a negative effect of high default rates on profitability; the positive effect of higher liquid assets ratios and, finally, the higher profitability of smaller, domestic private banks. The results are important to comprehend Brazilian banking institutions and can also help formulating and conducting monetary and regulatory policies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".