Characteristics of Real Futures Trading Networks

arXiv:1004.4402v2 [q-fin.ST] 27 Feb 2011 Characteristics of Real Futures Trading Networks Junjie Wanga,b, Shuigeng Zhoua,c,∗, Jihong Guand,∗ aSchool of Computer Science, Fudan University, Shanghai 200433, China bShanghai Futures Exchange, Shanghai 200122, China cShanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China dDepartment of Computer Science and Technology, Tongji University, Shanghai 201804, China 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 be- tweenness 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. Keywords: Complex networks, Futures trading networks, Scale-free scaling, Small-world effect PACS: 89.75.Fb, 89.75.Hc, 89.65.Gh 1. Introduction Since the works of Watts & Strogatz [1] and Barab´asi & Alberta [2] were published, complex networks, as a new scientific area, have attracted a tremendous amount of research interest [3– 7]. Complex networks can describe a wide range of real-life systems in nature and society, and show various nontrivial topological characteristics not occurring in simple networks such as regular lattices and random networks. There are a number of frequently cited examples that have been studied from the perspective of complex networks, including the World Wide Web [8– 11], the Internet [12], metabolic networks [13], scientific collaboration networks [14, 15], online social networks [16, 17], public transport networks [18, 19], airline flight networks [15, 20–23] and human language networks [24–26]. Empirical studies on these networks mentioned above ∗Corresponding author Email addresses: wangjunjie@fudan.edu.cn (Junjie Wang), sgzhou@fudan.edu.cn (Shuigeng Zhou), jhguan@tongji.edu.cn (Jihong Guan) Preprint submitted to Physics A May 12, 2018 have largely motivated the recent curiosity and concern about this new focus of research so that a number of techniques and models have been explored to improve people’s perception of topology and evolution of real complex systems [27–34]. With growing of importance and popularity, complex network theory has become a powerful tool with intuitive and effective representations to analyze complex systems in a variety of fields, including financial markets [35]. In the literature, a number of papers have been dedicated to studying financial markets from the perspective of complex networks. The major difference among these works lies in the types of networks to be constructed from financial data for characterizing the organization and struc- ture of financial markets. Some existing works constructed stock networks whose connectivity is defined by the correlation between any two time series of stock prices [36–40]. Some others es- tablished directed networks of stock ownership describing the relationship between stockholders and companies [41, 42]. Networks of market investors based on transaction interactions between the investors were also investigated. For example, Franke et al. [43] analyzed irregular trading behaviors of users in an experimental stock market, while Wang et al. [44] studied the evolving topology of such a network in an experimental futures exchange. Recently, Jiang et al. [45] has investigated stock weighted directed trading networks based on daily transaction records through reconstructing the limit order book with real order series from the Shenzhen Stock Exchange. The study on financial investor networks can provide clues for revealing the true complexity in financial markets, especially futures markets. In a real futures market, the futures trading model serves as a matching engine for executing all eligible orders from various market participants, and the interactions among the participants form a complex exchange network, which is termed as the futures trading network (FTN in short) in this paper. Simply put, a FTN consists of a set of trading participants, each of which has at least one connection of direct exchange

…(Full text truncated)…

This content is AI-processed based on ArXiv data.