Predicting investor funding behavior using crunchbase social network features

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 a...

[1]  Wee-Kheng Tan,et al.  An exploratory investigation of the investment information search behavior of individual domestic investors , 2012, Telematics Informatics.

[2]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[3]  Mark Grinblatt,et al.  The investment behavior and performance of various investor types: a study of Finland's unique data set , 2000 .

[4]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[5]  B. Bayus,et al.  Crowdfunding Creative Ideas: The Dynamics of Project Backers in Kickstarter , 2015 .

[6]  Param Vir Singh,et al.  Network Effects: The Influence of Structural Capital on Open Source Project Success , 2011, MIS Q..

[7]  M. Newman.  Clustering and preferential attachment in growing networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Christopher M. Bishop,et al.  Classification and regression , 1997 .

[9]  Dan Wang,et al.  Exposed: Venture Capital, Competitor Ties, and Entrepreneurial Innovation , 2014 .

[10]  David R. Peterson,et al.  Confidence, opinions of market efficiency, and investment behavior of finance professors , 2010 .

[11]  Theodoros Lappas,et al.  Finding a team of experts in social networks , 2009, KDD.

[12]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[13]  Aijun An,et al.  Discovering top-k teams of experts with/without a leader in social networks , 2011, CIKM '11.

[14]  R. H. Hamilton,et al.  E-commerce new venture performance: how funding impacts culture , 2001, Internet Res..

[15]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[16]  Christos Faloutsos,et al.  Using ghost edges for classification in sparsely labeled networks , 2008, KDD.

[17]  U. Hommel,et al.  Venture Capital Contracting in Theory and Practice: Implications for Entrepreneurship Research , 2016 .

[18]  Yuxian Eugene Liang,et al.  Where's the Money? The Social Behavior of Investors in Facebook's Small World , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[19]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[20]  D. Du,et al.  Influence Diffusion, Community Detection, and Link Prediction in Social Network Analysis , 2013 .

[21]  Przemyslaw Kazienko,et al.  Label-Dependent Feature Extraction in Social Networks for Node Classification , 2010, SocInfo.

[22]  Hassan Khosravi,et al.  A social network model of investment behaviour in the stock market , 2010 .

[23]  Efthimios Tambouris,et al.  Understanding the Predictive Power of Social Media This is a pre-print version of the following article : , 2013 .

[24]  Jure Leskovec,et al.  Signed networks in social media , 2010, CHI.

[25]  Bart Baesens,et al.  Identifying financially successful start-up profiles with data mining , 2011, Expert Syst. Appl..

[26]  Gary L. Lilien,et al.  Location, Location, Location: How Network Embeddedness Affects Project Success in Open Source Systems , 2006, Manag. Sci..

[27]  B. Uzzi,et al.  The Sources and Consequences of Embeddedness for the Economic Performance of Organizations: The Network Effect , 1996 .

[28]  Michael Pearson,et al.  Networks in family business: a multi-rational approach , 2014 .

[29]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Amir Barnea,et al.  Nature or Nurture: What Determines Investor Behavior? , 2010 .

[31]  Christopher M. Danforth,et al.  An evolutionary algorithm approach to link prediction in dynamic social networks , 2013, J. Comput. Sci..

[32]  Scott Shane,et al.  Network Ties, Reputation, and the Financing of New Ventures , 2002, Manag. Sci..

[33]  Carolyn Penstein Rosé,et al.  A Supervised Approach to Predict Company Acquisition with Factual and Topic Features Using Profiles and News Articles on TechCrunch , 2012, ICWSM.

[34]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[35]  Jure Leskovec,et al.  Predicting positive and negative links in online social networks , 2010, WWW '10.

[36]  Maria Elena Bontempi,et al.  Family firms’ investments, uncertainty and opacity , 2013 .

[37]  Rami Puzis,et al.  Link Prediction in Social Networks Using Computationally Efficient Topological Features , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[38]  Matthieu Latapy,et al.  Basic notions for the analysis of large two-mode networks , 2008, Soc. Networks.

[39]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[40]  Noah E. Friedkin,et al.  Horizons of Observability and Limits of Informal Control in Organizations , 1983 .

[41]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[42]  Andreas Krause,et al.  Cost-effective outbreak detection in networks , 2007, KDD '07.

[43]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[44]  Mehryar Mohri,et al.  AUC Optimization vs. Error Rate Minimization , 2003, NIPS.