The increasing role of hedge funds in Government of Canada bond auctions
Bibliographic record
Abstract
Over the past five years, the volume of Government of Canada (GoC) nominal bonds issued nearly doubled, from $122 billion in 2019–20 to $237 billion in 2024–25 (Bank of Canada 2025a).1 Despite this, the standard metrics of auction coverage and average yield show that GoC bond auctions have continued to perform well. We demonstrate that, starting in 2020, the increased participation of hedge funds at auctions matches the growth in GoC bond issuance. In fact, hedge funds now represent a significant investor class—the largest class after dealers. We find that hedge funds have responded to the higher dollar amount of GoC bond issuance because of their volume-based business models. This higher hedge fund participation—along with the fact they are more willing than other investor types to pay more for these bonds—explains the increased share of hedge funds’ auction allocation. We explain how hedge funds could be more likely than other types of investors to exit the market suddenly. We also show that their increased participation in auctions could strain the capacity of the balance sheets of dealers’ repurchase agreements (repos). This increased participation supports the cost-effective distribution of Canada’s domestic debt. However, it also represents a vulnerability that is important to acknowledge. Our analysis focuses on core bond tenors: 2, 5, 10 and 30 years. We use proprietary GoC bond auction data from 1999 to 2024 and identify the business type of bidders (including hedge funds) using an internal Bank of Canada list based on market intelligence about bidders’ business models.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".