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

Esposito C., Gortan M., Testa L., Chiaromonte F., Fagiolo G., Mina A., Rossetti G.

Computational Mathematics  Social and Information Networks (cs.SI)  Investment trajectory  Research  Computer Networks and Communications  Computer Science - Social and Information Networks  FOS: Computer and information sciences  Network analysis  Statistics - Applications  Venture capital  Applications (stat.AP)  Functional data analysis  Multidisciplinary 

In 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).

Source: Applied network science (2022). doi:10.1007/s41109-022-00482-y

Publisher: Springer international, Cham, Svizzera

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