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Venture capital investments through the lens of network and functional data analysis

Published version
Peer-reviewed

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Authors

Esposito, Christian 
Gortan, Marco 
Testa, Lorenzo 
Chiaromonte, Francesca 
Fagiolo, Giorgio 

Abstract

jats:titleAbstract</jats:title>jats:pIn this paper we characterize the performance of venture capital-backed firms based on their ability to attract investment. The aim of the study is to identify relevant predictors of success built from the network structure of firms’ and investors’ relations. Focusing on deal-level data for the health sector, we first create a bipartite network among firms and investors, and then apply functional data analysis to derive progressively more refined indicators of success captured by a binary, a scalar and a functional outcome. More specifically, we use different network centrality measures to capture the role of early investments for the success of the firm. Our results, which are robust to different specifications, suggest that success has a strong positive association with centrality measures of the firm and of its large investors, and a weaker but still detectable association with centrality measures of small investors and features describing firms as knowledge bridges. Finally, based on our analyses, success is not associated with firms’ and investors’ spreading power (harmonic centrality), nor with the tightness of investors’ community (clustering coefficient) and spreading ability (VoteRank).</jats:p>

Description

Keywords

46 Information and Computing Sciences, 4006 Communications Engineering, 40 Engineering, 4601 Applied Computing

Journal Title

Applied Network Science

Conference Name

Journal ISSN

2364-8228
2364-8228

Volume Title

7

Publisher

Springer Science and Business Media LLC
Sponsorship
European Commission (INFRAIA-01-2018-2019 n. 871042)