The evolution of k-shell in syndication networks reveals financial of venture institutions

Venture capital (VC) is a relatively newly emergent industry that is still subject to large uncertainties in China. Therefore, building a robust social network with other VC institutions is a good way to share information, various resources, and benefit from skill and knowledge complementarity to against risks. Strong evidences indicate that better networked VC institutions are of a better financial performance, however, most of previous works overlook the evolution of VC institutions and only focus on some simple topology indicators of the static syndication network, which also neglects higher-order network structure and cannot give a comprehensive evaluation. In this paper, based on VC investment records in the Chinese market, we construct temporal syndication networks between VC institutions year by year. As k-shell decomposition considers higher-order connection patterns, we employ k-shell as an evaluation of the influence of VC institutions in syndication networks. By clustering time series of k-shell values, the VC institutions in China fall into five groups that are quite different from each other on financial performances and investment behaviors. This, in turn, proves the power of our method that only based on proper sequential network properties, we can reveal their financial investment performance. Compared to other network centrality measurements, k-shell is a better indicator that is indicated by a smaller intra-group distance and a larger inter-group distance.

Automated summary

Venture capital is a rapidly growing industry in China, but it still faces uncertainties. This paper highlights the importance of building a strong social network among VC institutions to mitigate risks and improve financial performance. By analyzing temporal syndication networks based on VC investment records, the study shows that higher networked VC institutions tend to have better financial performance. The use of k-shell decomposition as an evaluation measure reveals distinct groups of VC institutions in China with different financial performance and investment behaviors.