Derivative Usage and Reporting Practices in Australian and Canadian Industries
Bibliographic record
Abstract
This study documents and compares derivative practices of Australian and Canadian non-financial firms over a five-year period from 2009 until 2013. We take advantage of improved derivative reporting practices after the introduction of International Financial Reporting Standards (IFRS) and compare country and industry derivative practices across both countries in a post-GFC environment. Our results show significant differences in the level of derivative usage between both countries and in contrast to earlier hedging studies, we observe that Canadian firms have a higher propensity to use financial derivatives. We also hypothesize that industry-hedging levels are not significantly different across both countries given a similar market and corporate governance environment. Consistent with this prediction, we find similarities in derivative usage for firms operating in the Industrials, Materials, Consumer Discretionary and Health Care industries. However, corporate derivative practices seem to be significantly different for Energy and IT firms in both countries over the sample period. This study takes advantage of improved derivative reporting since the introduction of the IFRS and extends previous research by providing detailed empirical evidence on international industry derivative usage.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".