Characteristics of Real Futures Trading Networks

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.

18 pages, 9 figures. Final version published in Physica A

References in corpus (9)

• Power-law distributions in empirical data
• A tool for filtering information in complex systems
• Graph Evolution: Densification and Shrinking Diameters
• Power-law Strength-Degree Correlation From a Resource-Allocation Dynamics on Weighted Networks
• Collective behavior of stock price movements in an emerging market
• Maximal planar scale-free Sierpinski networks with small-world effect and power-law strength-degree correlation
• Complex stock trading network among investors
• Weighted Network of Chinese Nature Science Basic Research
• Network Topology of an Experimental Futures Exchange

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• Stock portfolio structure of individual investors infers future trading behavior
• Random matrix approach to the dynamics of stock inventory variations
• Quantifying immediate price impact of trades based on the $k$-shell decomposition of stock trading networks