‘If the Facts Don't Fit the Theory … ’: The Security Design Puzzle in Venture Finance
Why this work is in the frame
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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 , C anada 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 ( VC s) investing in C anada. It has been documented that US VC s investing in C anada adopt a wide range of securities other than convertible stocks. If convertible securities truly represent the optimal form of VC finance, why would US VC s use different types of securities when investing in C anada? At present, researchers are still arguing about which factors would have the most significant impact on explaining the different financing behaviour of VC s 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 VC s around the world.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it