Venture Capital investments through the lens of Network and Functional Data Analysis

TL;DR

This study analyzes venture capital-backed firms using network and functional data analysis to identify predictors of success, highlighting the importance of centrality measures of firms and large investors.

Contribution

It introduces a novel application of network and functional data analysis to predict venture success based on deal-level data in the health sector.

Findings

Success correlates strongly with centrality of firms and large investors.

Weak association between success and small investors' centrality.

No significant link between success and spreading power or community tightness.

Abstract

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 (FDA) 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).