Hedging Policies to Reduce Agency Costs in Brazil
Bibliographic record
Abstract
Given the recent Brazilian economic scenario, characterized by political uncertainties and economic instabilities, it is essential for companies to engage in hedging as part of their financial policy in order to prevent their results from being affected by market frictions. In this context, the present study aimed to verify the impact of hedging on the agency costs of Brazilian companies. The methodology used was that of panel data contemplating a manually collected database of 154 companies between 2010 and 2017 (all companies that use derivatives for hedging). The results obtained agree with the literature on hedging and agency costs, indicating that the greater the use of hedging, the lower the agency costs faced by shareholders, expanding on the discussions involving developed markets. This relationship shows that by using hedging in a company’s financial policy, managers can minimize the impacts of market frictions and reduce the residual losses of shareholders in relation to what would otherwise occur.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".