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揭示风险投资联合网络的潜在结构。

Unveiling Latent Structure of Venture Capital Syndication Networks.

作者信息

Gu Weiwei, Yang Ao, Lu Lingyun, Li Ruiqi

机构信息

UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.

出版信息

Entropy (Basel). 2022 Oct 21;24(10):1506. doi: 10.3390/e24101506.

Abstract

Venture capital (VC) is a form of private equity financing provided by VC institutions to startups with high growth potential due to innovative technology or novel business models but also high risks. To against uncertainties and benefit from mutual complementarity and sharing resources and information, making joint-investments with other VC institutions on the same startup are pervasive, which forms an ever-growing complex syndication network. Attaining objective classifications of VC institutions and revealing the latent structure of joint-investment behaviors between them can deepen our understanding of the VC industry and boost the healthy development of the market and economy. In this work, we devise an iterative Loubar method based on the Lorenz curve to make objective classification of VC institutions automatically, which does not require setting arbitrary thresholds and the number of categories. We further reveal distinct investment behaviors across categories, where the top-ranked group enters more industries and investment stages with a better performance. Through network embedding of joint investment relations, we unveil the existence of possible territories of top-ranked VC institutions, and the hidden structure of relations between VC institutions.

摘要

风险投资(VC)是风险投资机构向具有高增长潜力的初创企业提供的一种私募股权融资形式,这些初创企业因创新技术或新颖商业模式而具有高增长潜力,但同时也伴随着高风险。为了应对不确定性,并从资源和信息的相互补充与共享中获益,与其他风险投资机构对同一初创企业进行联合投资的情况很普遍,这形成了一个不断增长的复杂联合投资网络。实现风险投资机构的客观分类,并揭示它们之间联合投资行为的潜在结构,能够加深我们对风险投资行业的理解,并推动市场和经济的健康发展。在这项工作中,我们设计了一种基于洛伦兹曲线的迭代 Loubar 方法,以自动对风险投资机构进行客观分类,该方法不需要设置任意阈值和类别数量。我们进一步揭示了不同类别之间独特的投资行为,其中排名靠前的群体进入更多行业和投资阶段,且表现更好。通过联合投资关系的网络嵌入,我们揭示了排名靠前的风险投资机构可能存在的领域,以及风险投资机构之间关系的隐藏结构。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fca0/9601518/807b00e04fef/entropy-24-01506-g0A1.jpg

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