An examination of US dollar risk management by Canadian non‐financial firms
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the impact of US dollar exchange rate risk on the value of Canadian non‐financial firms. Design/methodology/approach The sample, from the Compustat database, includes all non‐financial Canadian firms with sales over $100 million. The study segregates firms into hedging and non‐hedging groups and applies statistical techniques to test if hedging enhances value. Findings The results demonstrate that Canadian firms that have higher levels of US$ sales tend to use derivatives more frequently through higher levels of US$ exposure. Firms that have both US sales and assets appear less likely to use hedging. Firms with an American subsidiary and use financial instruments to hedge have higher values. When operational hedging is used with financial hedging, it is a value enhancing activity increasing their market‐to‐book by 14 per cent and market value‐to‐sales by 40 per cent. Incremental impact of these two hedging strategies is to enhance value by 7 per cent. Research limitations/implications The sample from Compustat captures large capitalization Canadian firms but ignores about 75 per cent of Canadian firms. There is a bias towards larger firms. Some hedging items are not disclosed on financial statements. A survey would enhance and complement these results. Practical implications The paper finds that it is important for Canadian firms that have exports denominated in US dollars to hedge their exposure. The full value of hedging is reaped by using both operational and financial hedges. Originality/value This study is the first that examines US dollar risk management by Canadian firms.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".