文献类型：期刊文献

英文题名：Complex stock trading network among investors

作者：Jiang, Zhi-Qiang[1,2,3];Zhou, Wei-Xing[1,2,3,4]

机构：[1]E China Univ Sci & Technol, Sch Business, 130 Meilong Rd,POB 114, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, Sch Sci, Shanghai 200237, Peoples R China;[3]E China Univ Sci & Technol, Res Ctr Econophys, Shanghai 200237, Peoples R China;[4]Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100080, Peoples R China

年份：2010

卷号：389

期号：21

起止页码：4929

外文期刊名：PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS

收录：;EI(收录号：20103513205077);WOS:【SSCI(收录号:WOS:000282241600050)，SCI-EXPANDED(收录号:WOS:000282241600050)】；

基金：We are grateful to Fei Ren and Gao-Feng Gu for fruitful discussions. This work was partially supported by the "Shu Guang" project (Grant no. 2008SG29) and the "Chen Guang" project (Grant no. 2008CG37) sponsored by Shanghai Municipal Education Commission and Shanghai Education Development Foundation, the Fundamental Research Funds for the Central Universities, and the Program for New Century Excellent Talents in University (Grant no. NCET-07-0288).

语种：英文

外文关键词：Econophysics; Limit order book; Trade sizes; Trading networks; Power-law distribution

摘要：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 Levy 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 of executed order as its fitness, the fitness model can reproduce the empirical power-law degree distribution. (C) 2010 Elsevier B.V. All rights reserved.