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Characteristics of Real Futures Trading Networks


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  • Junjie Wang
  • Shuigeng Zhou
  • Jihong Guan
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    Futures trading is the core of futures business, and it is considered as one of the typical complex systems. To investigate the complexity of futures trading, we employ the analytical method of complex networks. First, we use real trading records from the Shanghai Futures Exchange to construct futures trading networks, in which nodes are trading participants, and two nodes have a common edge if the two corresponding investors appear simultaneously in at least one trading record as a purchaser and a seller respectively. Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks. Empirical results show that the futures trading networks exhibit features such as scale-free behavior with interesting odd-even-degree divergence in low-degree regions, small-world effect, hierarchical organization, power-law betweenness distribution, disassortative mixing, and shrinkage of both the average path length and the diameter as network size increases. To the best of our knowledge, this is the first work that uses real data to study futures trading networks, and we argue that the research results can shed light on the nature of real futures business.

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    Bibliographic Info

    Paper provided by in its series Papers with number 1004.4402.

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    Date of creation: Apr 2010
    Date of revision: Feb 2011
    Publication status: Published in Physica A, Volume 390, Issue 2, 15 January 2011, Pages 398-409
    Handle: RePEc:arx:papers:1004.4402

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    1. S. C. Wang & J. J. Tseng & C. C. Tai & K. H. Lai & W. S. Wu & S. H. Chen & S. P. Li, 2008. "Network topology of an experimental futures exchange," The European Physical Journal B - Condensed Matter and Complex Systems, Springer, Springer, vol. 62(1), pages 105-111, 03.
    2. S. M.G. Caldeira & T. C. Petit Lob√£o & R. F.S. Andrade & A. Neme & J. G.V. Miranda, 2006. "The network of concepts in written texts," The European Physical Journal B - Condensed Matter and Complex Systems, Springer, Springer, vol. 49(4), pages 523-529, 02.
    3. Stefano Battiston & Joao F. Rodrigues & Hamza Zeytinoglu, 2007. "The Network Of Inter-Regional Direct Investment Stocks Across Europe," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., World Scientific Publishing Co. Pte. Ltd., vol. 10(01), pages 29-51.
    4. Zhou, Shuigeng & Hu, Guobiao & Zhang, Zhongzhi & Guan, Jihong, 2008. "An empirical study of Chinese language networks," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 387(12), pages 3039-3047.
    5. Fu, Feng & Liu, Lianghuan & Wang, Long, 2008. "Empirical analysis of online social networks in the age of Web 2.0," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 387(2), pages 675-684.
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    Cited by:
    1. Sun, Xiao-Qian & Cheng, Xue-Qi & Shen, Hua-Wei & Wang, Zhao-Yang, 2011. "Distinguishing manipulated stocks via trading network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 390(20), pages 3427-3434.
    2. Ming-Xia Li & Zhi-Qiang Jiang & Wen-Jie Xie & Xiong Xiong & Wei Zhang & Wei-Xing Zhou, 2013. "Unveiling correlations between financial variables and topological metrics of trading networks: Evidence from a stock and its warrant," Papers 1308.0925,
    3. Zhi-Qiang Jiang & Wen-Jie Xie & Xiong Xiong & Wei Zhang & Yong-Jie Zhang & W. -X. Zhou, 2012. "Trading networks, abnormal motifs and stock manipulation," Papers 1301.0007,
    4. Xiao-Qian Sun & Xue-Qi Cheng & Hua-Wei Shen & Zhao-Yang Wang, 2011. "Distinguishing manipulated stocks via trading network analysis," Papers 1110.2260,


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