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Venture Capital Investments Through the Lens of Network and Functional Data Analysis

Christian EspositoMarco GortanLorenzo TestaFrancesca ChiaromonteGiorgio FagioloAndrea MinaGiulio Rossetti

出版	LEM, Laboratory of Economics and Management, Institute of Economics, Scuola Superiore Sant'Anna, 2022
URL	http://books.google.com.hk/books?id=iy0azwEACAAJ&hl=&source=gbs_api

註釋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 in- vestors, and a weaker but still detectable association with centrality measures of small investors and features describing firms as knowl- edge 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).