Social Network Analysis tools have played instrumental role in extracting insights in a multitude of domains. The world of startups and investment has also used SNA tools to leverage the connection information of various forms to advantage. Startups and investment are building blocks of Industry and employment. Right investment decisions transform startups into big shot companies and generate large returns for the Investors. Taking good investment decisions involves estimating the risks, opportunities, trust levels and human resource talent. Startup success rate is also less, close to 10% by a research in UK, which all the more enhances the need to carefully evaluate the above factors.[1]
Many researches have explored the investor-startup, startup-startup, investor-investor networks to extract insights on the above mentioned factors. Several analyses have corroborated the existing understanding on some of the above factors. In the blog we discuss some of the researches in the domain. I will touch upon the underlying analyses, but will avoid rigorous details of the analyses.
Employees followed by Investor behavior [2]
The research is based on the use of Multiplex complex networks and network alignment technique to understand the how much the connections between companies, employees and investors is personnel. The methodology involved defining metrics and understanding the value changes on changing different thresholds.
Multiplex complex networks are characterized by individuals as nodes connected by different types of relationships.Network alignment is the process to understand similar structures in the graphs. Network alignment methods can be local v/s global, pairwise v/s individual, functional v/s topological.
The research considered a multiplex network of the employees of company and investors who invested in the same company. Nodes in the 2 network are connected if the investors invested in the company in which the employee worked. This way of defining necessitates the use of a temporal constraint while calculating metrics. The investment in the company should be made before the employee joined the company. If the investment was made before the employee joined the company, it is assumed that his contribution to attracting the investment is zero.
The research used 2 metrics to analyse the similarity, Jaccard Coefficient and intersection proportions. Jaccard coefficient was difficult to compare because of the un-normalized denominator. Intersection proportions did not have this limitation. 2 intersection proportions defined were, first indicative of the similarity of individual investor and employee nodes, and second indicative of similarity of individual employees and investor network.
Initial results indicated that the proportion of employees showing a behavior of being followed by investors is small. When restricted to only companies who received an investment and the investors who made investment in those companies, the proportion of the employees showing a behaviour of being followed by investors increased.The intensity of this behaviour is downplayed by bad or untalented employees and large institutional investors where a personnel connect with the employees is not frequent.
The results are in line with the notion that “Actors tend to engage in transactions with actors they have had interactions in the past”. Additionally, investment decisions by investors seem to be primarily driven by the talent of the founders.
Importance of structural network in Investor community [3]
The research explored the factors which determine the success of investments made by investors. Primarily factors coming from the geographical and common-investment network. The results indicate that both the networks play an important role in determining the success of investment.
Geographical network is important because investment success depends highly on the information access. Investors well placed in a network, i.e. centrally located, tend to have a higher and quicker access to information. The investment network is important because of the element of trust critical to investment decisions. Trust is dependent on the placement of the node in the network and hence insights from the analyses from the networks can have practical importance.
Importance of social network analysis on startup network for investment decision [4]
The work highlights the importance of a network analysis of the startups in addition to a financial analysis to make the investment decisions. The investment in the startup ecosystem has to be carefully analysed since it comes at the cost of liquidity as against investing in the listed equities market. Hence the trust is determined by the social network and there are relatively less checks-and-balances as compared to the listed equities market. For investors the network analysis is a way to deepen their insights on risks and opportunities.
Co-investment network is important for the investors to analyse and balance their portfolio based on correlation with other investors and their investment patterns. Information on the clusters in the network can help decide where to place the new investments so that the risk is diversified.Moreover the clusters also determine the ideas the companies in the cluster are exposed to and hence helping the investor understand the potential course of movement for the firm. Social capital calculated from the network can influence the corporate governance and policies of the company and mergers and Acquisitions.
Conclusion
It is remarkable to see the use of social network analysis for making investment decisions worth billions and to understand the information and ideas exposure to a company. This provides us with a systematic methodology to analyse the relationship information which is otherwise difficult to incorporate.
References
[1] “Insights that will turn your million dollar idea for a tech startup into a billion dollar idea” by Innovify, www.innovify.com
[2] Jessica Santana, Raine Hoover and Meera Vengadasubbu, 2014, “Multilayer Network Analysis of Investment Pattern”
[3] Sorenson, Olav and Stuart, Toby, Syndication Networks and the Spatial Distribution of Venture Capital Investments (December 15, 1999). Available at SSRN: http://ssrn.com/abstract=220451 or http://dx.doi.org/10.2139/ssrn.220451
[4] White paper by Sonean,October 2014, “Going beyond financial data - The crucial and complementary role of Social Network Analysis (SNA) for investment in startup companies”
I actually read it twice!
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