Financial networks with static and dynamic thresholds

Based on the daily data of American and Chinese stock markets, the dynamic behavior of a financial network with static and dynamic thresholds is investigated. Compared with the static threshold, the dynamic threshold suppresses the large fluctuation induced by the cross-correlation of individual stock prices, and leads to a stable topological structure in the dynamic evolution. Long-range time-correlations are revealed for the average clustering coefficient, average degree and cross-correlation of degrees. The dynamic network shows a two-peak behavior in the degree distribution.

[1]  Yue Yang,et al.  Complex network-based time series analysis , 2008 .

[2]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[3]  K. Kaski,et al.  Clustering and information in correlation based financial networks , 2003, cond-mat/0312682.

[4]  M. Oshikawa,et al.  Random matrix theory analysis of cross correlations in financial markets. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[6]  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.

[7]  R. Rosenfeld.  Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[8]  H. Stanley,et al.  Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. , 1995, Chaos.

[9]  V. Plerou,et al.  Quantifying and interpreting collective behavior in financial markets. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Bo Zheng,et al.  Cross-correlation in financial dynamics , 2009, 1202.0344.

[11]  M. Tumminello,et al.  Economic sector identification in a set of stocks traded at the New York Stock Exchange: a comparative analysis , 2006, SPIE International Symposium on Fluctuations and Noise.

[12]  R. May,et al.  Infection dynamics on scale-free networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[14]  Xueqi Cheng,et al.  Distinguishing manipulated stocks via trading network analysis , 2011, 1110.2260.

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

[16]  Zhi-Qiang Jiang,et al.  Statistical properties of world investment networks , 2008, 0807.4219.

[17]  R N Mantegna,et al.  Power-law relaxation in a complex system: Omori law after a financial market crash. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  H. Stanley,et al.  Fractals in Biology and Medicine: From DNA to the Heartbeat , 1994 .

[19]  Albert-László Barabási,et al.  Internet: Diameter of the World-Wide Web , 1999, Nature.

[20]  J. Bouchaud,et al.  Noise Dressing of Financial Correlation Matrices , 1998, cond-mat/9810255.

[21]  M. Marchesi,et al.  Scaling and criticality in a stochastic multi-agent model of a financial market , 1999, Nature.

[22]  T. Aste,et al.  Correlation based networks of equity returns sampled at different time horizons , 2007 .

[23]  F. Lillo,et al.  Topology of correlation-based minimal spanning trees in real and model markets. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  M Tumminello,et al.  A tool for filtering information in complex systems. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[25]  R Pastor-Satorras,et al.  Dynamical and correlation properties of the internet. , 2001, Physical review letters.

[26]  A. Barabasi,et al.  Quantifying social group evolution , 2007, Nature.

[27]  Fabrizio Lillo,et al.  Degree stability of a minimum spanning tree of price return and volatility , 2003 .

[28]  Fabrizio Lillo,et al.  Spanning Trees and bootstrap Reliability Estimation in Correlation-Based Networks , 2007, Int. J. Bifurc. Chaos.

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

[30]  S. Trimper,et al.  Minority games with score-dependent and agent-dependent payoffs. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  V. Plerou,et al.  Random matrix approach to cross correlations in financial data. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  G. Caldarelli,et al.  The fractal properties of Internet , 2000, cond-mat/0009178.

[33]  Shlomo Havlin,et al.  The structural role of weak and strong links in a financial market network , 2008, 0805.2477.

[34]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[35]  G. Caldarelli,et al.  Networks of equities in financial markets , 2004 .

[36]  L. da F. Costa,et al.  Characterization of complex networks: A survey of measurements , 2005, cond-mat/0505185.

[37]  B. Zheng,et al.  DYNAMIC RELAXATION OF FINANCIAL INDICES , 2009 .

[38]  R. Mantegna,et al.  Scaling behaviour in the dynamics of an economic index , 1995, Nature.

[39]  M. Mézard,et al.  Microscopic models for long ranged volatility correlations , 2001, cond-mat/0105076.

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

[41]  M. Newman,et al.  Why social networks are different from other types of networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[42]  V. Plerou,et al.  Scaling of the distribution of fluctuations of financial market indices. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[43]  B. Zheng,et al.  On return-volatility correlation in financial dynamics , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[44]  Thilo Gross,et al.  Adaptive coevolutionary networks: a review , 2007, Journal of The Royal Society Interface.

[45]  I.-M. Kim,et al.  Scale-Free Network in Stock Markets , 2002 .

[46]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

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

[48]  V. Plerou,et al.  Universal and Nonuniversal Properties of Cross Correlations in Financial Time Series , 1999, cond-mat/9902283.

[49]  M E J Newman.  Assortative mixing in networks. , 2002, Physical review letters.

[50]  Da-Ren He,et al.  The process of coevolutionary competitive exclusion: speciation, multifractality and power-laws in correlations , 2007 .