A Network Analysis of the Greek Stock Market

Abstract In this paper we analyse stock relationships in the Greek Stock Market. We propose a model that can depict such relationships and create networks of stocks. We investigate all stocks in the Greek Stock Market for years 2007 and 2012 (one year before and during the current economic crisis). Different networks are created according to the degree of correlation of stocks. These networks are visualized and evaluated, using methods from Social Network Analysis. A number of metrics, mainly centrality measurements, are calculated and interpreted. We discuss the hypothesis that Greek stocks follow the “herd” rule and investigate the role of important actors (stocks) in these networks. Our results show that the Greek Market is a “shallow” market, easily affected by a few big investors or the economic climate.

[1]  Jennifer Golbeck,et al.  Analyzing the Social Web , 2013 .

[2]  Deock-Ho Ha,et al.  Characteristics of networks in financial markets , 2007, Comput. Phys. Commun..

[3]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[4]  David F. Nettleton,et al.  Data mining of social networks represented as graphs , 2013, Comput. Sci. Rev..

[5]  Tom A. B. Snijders,et al.  Social Network Analysis , 2011, International Encyclopedia of Statistical Science.

[6]  Dimitris Kydros,et al.  A Network Analysis of the Greek Parliament and some Socio-Economic Issues , 2012 .

[7]  M E Newman,et al.  Scientific collaboration networks. I. Network construction and fundamental results. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Xintian Zhuang,et al.  A network analysis of the Chinese stock market , 2009 .

[9]  Ram Babu Roy,et al.  Identifying influential stock indices from global stock markets: A social network analysis approach , 2011, ANT/MobiWIS.

[10]  U. Brandes.  A faster algorithm for betweenness centrality , 2001 .

[11]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[12]  Matthieu Latapy,et al.  Main-memory triangle computations for very large (sparse (power-law)) graphs , 2008, Theor. Comput. Sci..

[13]  Jennifer Golbeck.  Chapter 4 – Network Visualization , 2013 .

[14]  Raj Kumar Pan,et al.  Collective behavior of stock price movements in an emerging market. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Stefan Burr,et al.  The Mathematics of networks , 1982 .

[16]  Panos M. Pardalos,et al.  Mining market data: A network approach , 2006, Comput. Oper. Res..

[17]  P. Holland,et al.  TRANSITIVITY IN STRUCTURAL MODELS OF SMALL GROUPS , 1977 .

[18]  Benjamin Miranda Tabak,et al.  Quantifying price fluctuations in the Brazilian stock market , 2009 .

[19]  L. Amaral,et al.  Small-World Networks: Evidence for a Crossover Picture , 1999, cond-mat/9903108.

[20]  R. Mantegna.  Hierarchical structure in financial markets , 1998, cond-mat/9802256.