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A network analysis of the Chinese stock market

机译：中国股票市场的网络分析

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摘要

In many practical important cases, a massive dataset can be represented as a very large network with certain attributes associated with its vertices and edges. Stock markets generate huge amounts of data, which can be use for constructing the network reflecting the market's behavior. In this paper, we use a threshold method to construct China's stock correlation network and then study the network's structural properties and topological stability. We conduct a statistical analysis of this network and show that it follows a power-law model. We also detect components, cliques and independent sets in this network. These analyses allows one to apply a new data mining technique of classifying financial instruments based on stock price data, which provides a deeper insight into the internal structure of the stock market. Moreover, we test the topological stability of this network and find that it displays a topological robustness against random vertex failures, but it is also fragile to intentional attacks. Such a network stability property would be also useful for portfolio investment and risk management.
机译：在许多实际的重要情况下，海量数据集可以表示为具有与其顶点和边相关联的某些属性的非常大的网络。股票市场会生成大量数据，这些数据可用于构建反映市场行为的网络。在本文中，我们使用阈值方法构建了中国的股票相关网络，然后研究了该网络的结构特性和拓扑稳定性。我们对该网络进行了统计分析，并表明它遵循幂律模型。我们还检测该网络中的组件，集团和独立集合。这些分析使人们可以应用一种新的数据挖掘技术，该技术基于股票价格数据对金融工具进行分类，从而可以更深入地了解股票市场的内部结构。此外，我们测试了该网络的拓扑稳定性，发现它对随机顶点故障显示出拓扑稳健性，但对于故意攻击也很脆弱。这种网络稳定性属性对于证券投资和风险管理也很有用。

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