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Characteristics of real futures trading networks

Author

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  • Wang, Junjie
  • Zhou, Shuigeng
  • Guan, Jihong

Abstract

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.

Suggested Citation

  • Wang, Junjie & Zhou, Shuigeng & Guan, Jihong, 2011. "Characteristics of real futures trading networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(2), pages 398-409.
  • Handle: RePEc:eee:phsmap:v:390:y:2011:i:2:p:398-409
    DOI: 10.1016/j.physa.2010.09.027
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    Citations

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    Cited by:

    1. Li, Ming-Xia & Jiang, Zhi-Qiang & Xie, Wen-Jie & Xiong, Xiong & Zhang, Wei & Zhou, Wei-Xing, 2015. "Unveiling correlations between financial variables and topological metrics of trading networks: Evidence from a stock and its warrant," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 575-584.
    2. 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, vol. 390(20), pages 3427-3434.
    3. Sun, Xiao-Qian & Shen, Hua-Wei & Cheng, Xue-Qi & Zhang, Yuqing, 2017. "Detecting anomalous traders using multi-slice network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 1-9.
    4. Zhao, Zheng & Zhang, YongJie & Feng, Xu & Zhang, Wei, 2014. "An analysis of herding behavior in security analysts’ networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 116-124.
    5. Hao, Xiaoqing & An, Haizhong & Qi, Hai & Gao, Xiangyun, 2016. "Evolution of the exergy flow network embodied in the global fossil energy trade: Based on complex network," Applied Energy, Elsevier, vol. 162(C), pages 1515-1522.
    6. 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.
    7. Zhong, Weiqiong & An, Haizhong & Gao, Xiangyun & Sun, Xiaoqi, 2014. "The evolution of communities in the international oil trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 42-52.
    8. 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, arXiv.org.
    9. 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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