Venture Capital Investment Strategy and Portfolio Failure Rate: A Longitudinal Study
Bibliographic record
Abstract
In this study, longitudinal data are used to examine theeffects of venture capital firms' (VCFs) investment strategies on the failurerates in their portfolios. Particular attention is paid to two specificstrategic choices faced by VCFs—the choice to develop specialized expertise andthe choice to invest together with syndicate partners. First, a theoretical framework for VCFs' contribution to new venturesurvival is used to generate two hypotheses, the first of which proposes thatthe development of a special expertise through an investment specializationstrategy decreases the proportion of failures in a VCF's portfolio. The secondhypothesis predicts the effects of management by a syndicate on failurerates. Data on 200 U.S.-based VCFs that have invested in at least 20 portfoliocompanies are used to test the hypotheses. Spanning the years 1990-2001, thedata reveal that VCFs' specialized expertise decreases the relative number offailures in their portfolio. They also suggest that investment syndicatesincrease the chances that portfolio firms will fail. (SAA)
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".