Predicting investor funding behavior using crunchbase social network features

Abstract

Purpose – What makes investors tick? Largely counter-intuitive compared to the findings of most past research, this study explores the possibility that funding investors invest in companies based on social relationships, which could be positive or negative, similar or dissimilar. The purpose of this paper is to build a social network graph using data from CrunchBase, the largest public database with profiles about companies. The authors combine social network analysis with the study of investing behavior in order to explore how similarity between investors and companies affects investing behavior through social network analysis. Design/methodology/approach – This study crawls and analyzes data from CrunchBase and builds a social network graph which includes people, companies, social links and funding investment links. The problem is then formalized as a link (or relationship) prediction task in a social network to model and predict (across various machine learning methods and evaluation metrics) whether an investor will create a link to a company in the social network. Various link prediction techniques such as common neighbors, shortest path, Jaccard Coefficient and others are integrated to provide a holistic view of a social network and provide useful insights as to how a pair of nodes may be related (i.e., whether the investor will invest in the particular company at a time) within the social network. Findings – This study finds that funding investors are more likely to invest in a particular company if they have a stronger social relationship in terms of closeness, be it direct or indirect. At the same time, if investors and companies share too many common neighbors, investors are less likely to invest in such companies. Originality/value – The author’s study is among the first to use data from the largest public company profile database of CrunchBase as a social network for research purposes. The author ' s also identify certain social relationship factors that can help prescribe the investor funding behavior. Authors prediction strategy based on these factors and modeling it as a link prediction problem generally works well across the most prominent learning algorithms and perform well in terms of aggregate performance as well as individual industries. In other words, this study would like to encourage companies to focus on social relationship factors in addition to other factors when seeking external funding investments.

Relation between start-ups’ online social media presence and fundraising

“…Start-ups face the challenge of gaining awareness and trust of people, especially investors', to get access to necessary resources (Liang & Yuan, 2016). Wu (2007) argued that network building increases the chance to gain funding in general (Stam & Elfring, 2008).…”

“…Previous studies also indicate that online social networks are connected to offline social networks (Subrahmanyam et al, 2008). While the topic of funding investments is one of the most widely discussed topics in the realm of investing and business, few studies provide evidence as to how start-ups can increase funding investments from investors (Liang & Yuan, 2016). One way to understand how start-ups can increase their chances of receiving funding investment from investors is to understand one's business networking behavior.…”

“…Previous studies mainly focused on social media as a marketing tool for promoting products and attracting consumers, but recently a growing number of entrepreneurs have realized its potential in attracting funding (Clark & Melancon, 2013;Kadam & Ayarekar, 2014). According to Liang and Yuan (2016), entrepreneurs with broader social networks are more likely to receive funding from investors and accomplish business development. On the other hand, Tan and Tan (2012) explored the roles played by online and offline communities and discovered that offline communities have greater influence on investing behaviors.…”

“…Hong (2013) discussed that an increasing number of venture capitalists rely on social media to monitor promising startups' social-media efforts to assess their investment potential. Other studies also provided evidence as to how entrepreneurs can increase funding investments from investors by using social media (Alexy et al, 2012;Liang & Yuan, 2016).…”

Mass Producing Innovation: A Case Investigation on Why Accelerators Might Not Be a Paradox

“…Data was sourced from Crunch base. Crunch base provided the largest public dataset on startup companies, including 991 YC companies (almost all) (Liang & Yuan, 2016). Crunch base included information on a company's starting date, the dates and amounts of investment rounds, and details on acquisitions.…”