Bibliographic record
Abstract
We investigate the determinants of the risk management decision for an original dataset of North American gold mining firms. We propose explanations based on the firm's financial characteristics, managerial risk aversion and internal corporate governance mechanisms. We develop a theoretical model in which the debt and the hedging decisions are made simultaneously. Our model suggests that more hedging does not always lead to a higher debt capacity when the firm holds a standard debt contract, while hedging is an increasing function of the firm's financial distress costs. We then test the predictions of our model. To estimate our system of simultaneous Tobit equations, we extend, to panel data, the minimum distance estimator proposed by Lee (1995). We obtain that financial distress costs, information asymmetry, separation between the posts of CEO and chairman of the board positions and managerial risk aversion are important determinants of the decision to hedge whereas the composition of the board of directors has no impact in such decision. Also, our results do not support the conclusion that firms hedge in order to increase their debt capacity which seems to confirm our model's prediction.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".