Cross-border VC investment in Canadian firms: implications for exit patterns
Bibliographic record
Abstract
Over the last few years, growth in the flow of venture capital (VC) in Canada has been driven primarily by increased reliance on foreign, primarily US, investors This is a situation that is not unique to Canada. Other countries (for example, Ireland and several EU nations) have small domestic VC stocks but are geographically situated near countries with relatively large stocks of VC. This paper reports research that shows this to be a mixed blessing. On the one hand, foreign investors make relatively large investments, thereby addressing the downward-skewed size distribution of VC funds in the Canadian VC market. Moreover, compared with domestic investors, foreign VCs’ participation is associated with higher propensities of successful exits through IPOs, greater capital availability, and shorter time to exit. On the other hand, this research also documents a relationship between foreign VCs’ participation and lower payments at exit per dollar of VC investment, raising concerns about the monetary returns to Canadian founders and early-stage, higher risk, Canadian syndicate VCs. The link between cross-border VC investment and higher likelihood of VC exit through cross-border M&As is also noteworthy. These empirical findings address the role of foreign VCs in financing Canadian growth firms, and help provide a yet more comprehensive understanding of the Canadian VC market.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".