Adverse Selection and Capital Structure: Evidence from Venture Capital
Bibliographic record
Abstract
Venture capitalists (VCs) in all non–U.S. countries around the world have consistently reported the use of a variety of securities, including common equity, preferred equity, convertible preferred equity, debt, convertible debt, and combinations (in the U.S., VCs typically use convertible preferred equity, and there is a tax bias in favor of that instrument in the U.S.). The types of entrepreneurial firms that receive venture finance may be defined by a variety of characteristics, such as stage of development, type of industry, and capital requirements. Given this broad context observed in practice, previous research has not considered the extent to which different securities, among the complete class of forms of finance, attract different types of entrepreneurial firms. In this article, we investigate the empirical tractability of the adverse selection risks associated with capital structure from 4,114 first–round Canadian venture capital investments. We first characterize the nature of uncertainty (in terms of the risk of financing a lemon or a nut) facing investors for different types of entrepreneurial firms. We then show that VC syndication significantly mitigates problems of adverse selection.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".