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We examine how government venture capital (GVC)—a specific type of political connection—affected initial public offering (IPO) valuation. Contrary to the well-recognized benefits of political connections in channeling access to financial resources in China, our analysis of 959 IPOs between 2008 and 2014 suggests that GVC backing lowers IPO valuation. This baseline effect is moderated by other sources of political connection (e.g. government ownership, state sector experience of top management team members, private sector partner status, and institutional environments). We argue that it is the negative signaling mechanism revolving around political connections that accounts for this observable pattern. This research enriches the signaling theory by uncovering signal emergence and analyzing the interactions between several signaling sources of political connection. Specifically, it contributes to a better understanding of political connections by specifying an undesirable consequence of state-led financialization, which has timely practical relevance as China's capital market is steering toward a rule-based system. [ABSTRACT FROM AUTHOR]

This paper explores the different syndication behavior in various types of Chinese venture capitals (VC), in particular it focuses on foreign, private-owned and state-owned VC firms and these firms' behavior when faced with institutional uncertainties. Mixed methods data analysis was conducted using the Simuton database of 1,173 VC firms and snowball interviews were also conducted in the Chinese state-owned and private-owned VCs as well as foreign VCs. Findings indicate foreign VC investors are more willing to build much denser relationships than those of Chinese state-owned and private VC firms in order to overcome the uncertainty. This result provides additional evidence that adopting close relationships or a guanxi-based (informal social network) strategy does not only depend on cultural issues such as East Asian culture, but also on high institutional uncertainty. Furthermore, this paper develops four propositions on how VC firms establish syndication influenced by high institutional uncertainty. Finally, the conclusion and discussion sections put forward implications to VC companies about how informal social networks facilitate the internationalization process and growth in foreign markets. [ABSTRACT FROM AUTHOR]

Venture capital (VC) is of great importance to high-tech industry and network economy since many high-tech firms benefit from VC, especially when they are in their infancy, such as Google, PayPal, and Alibaba. Over 80 percent of the VC investments are related to at least two investors and so co-investment is an important phenomenon in the VC market. However, it is challenging to predict future co-investments due to the complexity and uncertainty of VC behavior. In this paper, we formulate the problem of co-investment prediction into a factor graph model incorporating structural balance theory. We design a large number of features from the perspective of both domain knowledge and social network, and select prominent features by group Lasso. In this paper, we introduce two new investment datasets for the study of VC. Experiment results demonstrate that the proposed model significantly (+9% in terms of accuracy) outperforms the baseline methods. It is shown that only the top 10 features selected by group Lasso (e.g., nationality, number of common neighbors, betweenness, shortest distance, investor type, number of invested fields, and Jaccard similarity of invested fields) can explain the formation of the VC network quite well (around 90 percent in terms of accuracy). In addition, we have some interesting findings. For instance, in the VC network, the co-investor of my co-investor tends to be my co-investor; VC pairs from the same country, of the same investor type, with short distance, with more common neighbors or with appropriate Jaccard similarity of invested fields are likely to co-invest; VCs of large betweenness or of a large number of invested fields have advantage in the VC network; investors of Asian countries, especially of China, are more likely to have social relations than other countries. [ABSTRACT FROM AUTHOR]

Computational social science has integrated social science theories and methodology with big data analysis. It has opened a number of new topics for big data analysis and enabled qualitative and quantitative sociological research to provide the ground truth for testing the results of data mining. At the same time, threads of evidence obtained by data mining can inform the development of theory and thereby guide the construction of predictive models to infer and explain more phenomena. Using the example of the Internet data of China's venture capital industry, this paper shows the triadic dialogue among data mining, sociological theory, and predictive models and forms a methodology of big data analysis guided by sociological theories. [ABSTRACT FROM AUTHOR]