Understanding Venture Capital Syndication in Information Technology Sectors: A Network Formation Perspective
2026-08-28 • Social and Information Networks
Social and Information Networks
AI summaryⓘ
The authors studied how venture capital investors in the U.S. information technology sector choose partners to invest with each year. They found that previous partnerships are the strongest predictor of future co-investment, along with sharing mutual partners, being close geographically, and belonging to similar types of organizations. Their analysis also showed that networks tend to form closed triangles and investors often prefer partners like themselves. The study helps explain how relationships and similarities shape investment networks over time.
venture capital syndicationco-investmentnetwork embeddednessdyadic logit modeltriadic closurehomophilyPitchBookinformation technology venture financeorganizational similaritygeographic proximity
Authors
Liheng Tan, Zhengkai Tu, Prasanna Karhade
Abstract
Venture capital syndication enables investors to pool diligence, share risk, and signal venture quality, while shaping the relationships through which investment networks develop. We examine how prior relationships, network embeddedness, and organizational similarity structure annual co-investment link formation in U.S. information technology venture finance. Using dyad-complete PitchBook panels for the hardware, software, and hybrid subsectors from 1966 to 2024, we test seven mechanisms through full-sample dyadic logit models with dyad-clustered standard errors. Across subsectors, prior collaboration is the most consistent correlate of co-investment; shared partners, geographic proximity, and organizational-type similarity are also positively associated with link formation, while domain overlap, prominence, and experience vary across settings. A static ERGM of the 2024 software network among 1,100 persistently active investors likewise produces positive estimates for triadic closure and geographic homophily and a smaller positive estimate for type homophily. By combining complete dyadic risk sets with a whole-network specification, the study shows how relational persistence, network closure, and homophily jointly structure IT venture syndication networks. In future work, we will extend the analysis with temporal network models, counterfactual simulations of market shocks, and evaluations of network-aware partner recommendations.