Count Models of Social Networks in Finance

Harrison G. Hong

Princeton University - Department of Economics; National Bureau of Economic Research (NBER)

Jiangmin Xu

Princeton University

November 29, 2013

We use overdispersed Poisson regression models to study social networks in finance. We count an investor's social connections in different groups, such as cities or industries, as proportional to the number of stocks held by this investor that are headquartered in those cities or part of those industries. When connections are formed randomly, the count of such connections in any group follows a Poisson distribution. Using data from institutional and retail investors' holdings, we find instead overdispersion for some groups. It implies that investors have distinct propensities to form ties because they are part of networks. We relate these propensities to investor demographics, gauge the prevalence of city versus industry networks, and measure their value for investor performance. These models can be utilized to study any financial network where investment data are available.

Number of Pages in PDF File: 47

Keywords: Social Networks, Poisson Regressions, Investor Behavior

JEL Classification: G1, G2, G3

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Date posted: January 8, 2014  

Suggested Citation

Hong, Harrison G. and Xu, Jiangmin, Count Models of Social Networks in Finance (November 29, 2013). Available at SSRN: http://ssrn.com/abstract=2375461 or http://dx.doi.org/10.2139/ssrn.2375461

Contact Information

Harrison G. Hong (Contact Author)
Princeton University - Department of Economics ( email )
Princeton, NJ 08544-1021
United States
National Bureau of Economic Research (NBER)
1050 Massachusetts Avenue
Cambridge, MA 02138
United States
Jiangmin Xu
Princeton University ( email )
001 Fisher Hall
Department of Economics
Princeton, NJ 08544
United States
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