‘If the Facts Don't Fit the Theory … ’: The Security Design Puzzle in Venture Finance
Bibliographic record
Abstract
When confronting theory with evidence, divergent results surface with reference to the optimal securities that should be adopted in venture capital (VC) finance. The vast majority of the theoretical models on VC consistently predict that convertible securities, especially in the form of convertible preferred stocks, represent the optimal form of finance. While the theoretical literature seems to be supported by empirical studies in the US, the evidence outside the US shows the opposite results. Puzzling patterns emerge, especially when comparing the evidence from the US, Canada and Europe, and an intensive academic debate is under way. The evidence becomes even more challenging when considering the contrasting financing behaviour of US venture capitalists (VCs) investing in Canada. It has been documented that US VCs investing in Canada adopt a wide range of securities other than convertible stocks. If convertible securities truly represent the optimal form of VC finance, why would US VCs use different types of securities when investing in Canada? At present, researchers are still arguing about which factors would have the most significant impact on explaining the different financing behaviour of VCs around the world. The purpose of this paper is to shed some light on the ongoing international debate on the optimal security design and contracting behaviour in venture finance. With this review, the authors intend to contribute to the VC literature by identifying current trends, explanations and determinants underlying the puzzling empirical evidence on the financing structure adopted by VCs around the world.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".