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摘要翻译：
本文以深圳证券交易所上市的一只高流动性股票（深圳发展银行）2003年全年的订单流数据为基础，对复杂的股票交易网络进行了实证研究，旨在揭示复杂的股票交易网络的统计特性。通过对限价指令书的重构，我们可以提取出每个交易日内每个执行指令的详细信息，并证明了不同交易日的交易规模分布呈现幂律尾数，且大多数估计的幂律指数都在L{e}vy稳定区间内。根据投资者之间的订单匹配记录，构建一个每个交易日的股票交易网络，将投资者映射为节点，将每笔交易转化为从卖方到买方的直接边，并以交易规模为权重。我们发现所有的交易网络由一个巨大的组成部分组成，具有幂律度分布和不协调的体系结构。特别是，阶数与阶数之间的关系是幂律函数。该适应度模型以执行顺序大小作为适应度，可以再现经验幂律度分布。
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英文标题：
《Complex stock trading network among investors》
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作者：
Zhi-Qiang Jiang (ECUST) and Wei-Xing Zhou (ECUST)
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最新提交年份：
2010
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分类信息：
一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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英文摘要：
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 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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PDF链接：
https://arxiv.org/pdf/1003.2459
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