Where's the Money? The Social Behavior of Investors in Facebook's Small World

Are investing activities dependent on social relationships? In our research, we apply social network analysis to the field of investing behaviors and discover that investors have a tendency to invest in companies that are socially similar to them. While traditional studies on investing behavior tend to focus on factors like psychology, opinions, investing experience etc, they fail to consider social relationship as an important factor. In this paper we provide general rules of thumb that are useful for companies seeking funding from investor. These rules of thumb are generated by analyzing the social relationships between investors and companies found within the small world of Facebook.

[1]  Armin Schwienbacher,et al.  Are Private Equity Funds Risk-Takers? Evidence From a Comparison of Novice and Established Funds , 2013 .

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

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

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

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

[6]  Stanley Milgram,et al.  An Experimental Study of the Small World Problem , 1969 .

[7]  Mark S. Granovetter The Strength of Weak Ties , 1973, American Journal of Sociology.

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

[9]  Aidong Zhang,et al.  Bridging centrality: graph mining from element level to group level , 2008, KDD.

[10]  L. Freeman Centrality in social networks conceptual clarification , 1978 .

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

[12]  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.

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

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

[15]  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.

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

[17]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[18]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

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

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

[21]  Jure Leskovec,et al.  Supervised random walks: predicting and recommending links in social networks , 2010, WSDM '11.

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

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

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

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

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

[27]  Marco Rosa,et al.  Four degrees of separation , 2011, WebSci '12.