Venture-capital syndication: improved venture selection vs. the value-added S.R. Das et
Bibliographic record
Abstract
Syndication arises when venture capitalists jointly invest in projects. We model and test two possible reasons for syndication: project selection, as an additional venture capitalist provides an informative second opinion; and complementary management skills of additional venture capitalists. The cen-tral question is whether venture capitalists are engaged primarily in selection or in managerial value added. These alternatives imply contrasting predic-tions about comparative returns to syndicated and standalone investments. Our empirical analysis, using Canadian data, finds that syndicated invest-ments have higher returns, favoring the value-added interpretation. We also discuss risk sharing and project scale as possible reasons for syndication. James Brander and Werner Antweiler are in the Faculty of Commerce at the University of British Columbia. Raphael Amit is in the Wharton School at the University of Penn-sylvania. We are very grateful to Mary Macdonald of Macdonald & Associates Ltd. (www.canadavc.com) for providing access on an anonymous basis to the data used in this paper. We also thank two referees and a coeditor for very valuable comments. In addition, workshop participants at Stanford University, the University of British Columbia, and UC Berkeley have, along with others, made many helpful suggestions. We would, in particular, like to acknowledge specific contributions from Keith Head, Chris Hennessy, Alan Kraus, Hayne Leland, Paul Pfleiderer, and Manju Puri. All three authors are affiliated with the W. Maurice Young Entrepreneurship and Venture Capi-tal Centre at the University of British Columbia and are very appreciative of financial
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".