摘要翻译：
通过比较原始股票网络和由随机矩阵理论(RMT)建立的相关矩阵估计的股票网络，研究了股票网络的拓扑性质。我们使用了在韩国、日本、加拿大、美国、意大利和英国市场指数上交易的个股。结果如下。由于相关矩阵反映了更多的特征值性质，由相关矩阵估计出的股票网络与原始股票网络的一致性程度逐渐提高。与原始股票网络中其他股票链接数量不同的每个股票显示出不同的响应。特别是，最大特征值在股票网络的形成方面是一个重要的确定性因素。
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英文标题：
《Topological Properties of Stock Networks Based on Random Matrix Theory
  in Financial Time Series》
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作者：
Cheoljun Eom, Gapjin Oh, Hawoong Jeong, Seunghwan Kim
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最新提交年份：
2007
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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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一级分类：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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英文摘要：
  We investigated the topological properties of stock networks through a comparison of the original stock network with the estimated stock network from the correlation matrix created by the random matrix theory (RMT). We used individual stocks traded on the market indices of Korea, Japan, Canada, the USA, Italy, and the UK. The results are as follows. As the correlation matrix reflects the more eigenvalue property, the estimated stock network from the correlation matrix gradually increases the degree of consistency with the original stock network. Each stock with a different number of links to other stocks in the original stock network shows a different response. In particular, the largest eigenvalue is a significant deterministic factor in terms of the formation of a stock network.
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PDF链接：
https://arxiv.org/pdf/0709.2209