EARNINGS MANAGEMENT AND INCOME SMOOTHING PRACTICES: A PANEL DATA REGRESSION MODEL WITH BRAZILIAN CREDIT COOPERATIVES
Bibliographic record
Abstract
This paper aims to analyze if there is evidence of Earnings Management practices, related to losses from credit operations of Brazilian credit unions. The research uses a quantitative and inferential approach, with regression analysis using panel data analysis, with a sample of 670 single credit unions regulated by the Brazilian Central Bank. These credit unions were observed between the first quarter of 2010 and the last quarter of 2019, totalizing an amount of 26800 observations. The results indicate that credit unions manage their results in the Income Smoothing modality to avoid their variability, to give greater confidence and solidity to the market. Data also shows that changes in the regulatory standards influence Earnings Management practices by setting up an allowance for loan losses. Results show that free admission cooperatives are more likely to manage their results through credit loss provisions when compared to restricted admission cooperatives. Thus, it is possible to conclude that there is earnings management in credit unions, and therefore the need for greater control by the governance bodies of these entities.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".