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Complex stock trading network among investors

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  • Zhi-Qiang Jiang

    (ECUST)

  • Wei-Xing Zhou

    (ECUST)

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    Abstract

    We provide an empirical investigation aimed at uncovering the statistical properties of intricate stock trading networks based on the order flow data of a highly liquid stock (Shenzhen Development Bank) listed on Shenzhen Stock Exchange during the whole year of 2003. By reconstructing the limit order book, we can extract detailed information of each executed order for each trading day and demonstrate that the trade size distributions for different trading days exhibit power-law tails and that most of the estimated power-law exponents are well within the L{\'e}vy stable regime. Based on the records of order matching among investors, we can construct a stock trading network for each trading day, in which the investors are mapped into nodes and each transaction is translated as a direct edge from the seller to the buyer with the trade size as its weight. We find that all the trading networks comprise a giant component and have power-law degree distributions and disassortative architectures. In particular, the degrees are correlated with order sizes by a power-law function. By regarding the size executed order as its fitness, the fitness model can reproduce the empirical power-law degree distribution.

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    File URL: http://arxiv.org/pdf/1003.2459
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number 1003.2459.

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    Date of creation: Mar 2010
    Date of revision: May 2010
    Publication status: Published in Physica A 389 (2010) 4929?4941
    Handle: RePEc:arx:papers:1003.2459

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    Web page: http://arxiv.org/

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    1. Dorogovtsev, S.N. & Mendes, J.F.F., 2003. "Evolution of Networks: From Biological Nets to the Internet and WWW," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780198515906, October.
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    Cited by:
    1. 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, arXiv.org.
    2. Wang, Junjie & Zhou, Shuigeng & Guan, Jihong, 2011. "Characteristics of real futures trading networks," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 390(2), pages 398-409.
    3. Yang, Chunxia & Chen, Yanhua & Niu, Lei & Li, Qian, 2014. "Cointegration analysis and influence rank—A network approach to global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 400(C), pages 168-185.
    4. 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.
    5. Xiao-Qian Sun & Xue-Qi Cheng & Hua-Wei Shen & Zhao-Yang Wang, 2011. "Distinguishing manipulated stocks via trading network analysis," Papers 1110.2260, arXiv.org.

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