论文标题
中国股票市场的信息流网络
Information flow networks of Chinese stock market sectors
论文作者
论文摘要
转移熵测量不同时间序列之间信息流的强度和方向。我们研究中国股票市场的信息流网络，并确定重要的部门和信息流道。本文使用来自2000年至2017年的Shenyin \＆Wanguo证券的28级1级部门的每日收盘价数据来研究不同部门之间的信息传播。我们将信息流网络与扇区构建为节点，以及它们之间的转移熵作为相应的边缘。然后，我们采用最大跨越树木（MSA）来提取重要信息流和网络的层次结构。我们发现，在整个样本期间，\ textIt {Composite}扇区是整个股票市场的信息来源，而\ textit {non-Bank Financial}扇区是信息汇。 We also find that the \textit{non-bank finance}, \textit{bank}, \textit{computer}, \textit{media}, \textit{real estate}, \textit{medical biology} and \textit{non-ferrous metals} sectors appear as high-degree root nodes in the outgoing and incoming information flow MSAs.尤其是，\ textit {non-bank Finance}和\ textit {bank}扇区在2008年以后在传出信息流网络中具有显着高度。我们发现股票市场动荡如何影响MSA的结构。最后，我们揭示了信息源和下沉扇区的特异性，并得出一个结论，将根节点扇区作为传入信息流网络的信息接收器。总体而言，我们的分析表明，信息流网络的结构随时间而变化，市场表现出扇形旋转现象。我们的工作对市场参与者和政策制定者在管理市场风险和控制风险传染方面具有重要意义。
Transfer entropy measures the strength and direction of information flow between different time series. We study the information flow networks of the Chinese stock market and identify important sectors and information flow paths. This paper uses the daily closing price data of the 28 level-1 sectors from Shenyin \& Wanguo Securities ranging from 2000 to 2017 to study the information transmission between different sectors. We construct information flow networks with the sectors as the nodes and the transfer entropy between them as the corresponding edges. Then we adopt the maximum spanning arborescence (MSA) to extracting important information flows and the hierarchical structure of the networks. We find that, during the whole sample period, the \textit{composite} sector is an information source of the whole stock market, while the \textit{non-bank financial} sector is the information sink. We also find that the \textit{non-bank finance}, \textit{bank}, \textit{computer}, \textit{media}, \textit{real estate}, \textit{medical biology} and \textit{non-ferrous metals} sectors appear as high-degree root nodes in the outgoing and incoming information flow MSAs. Especially, the \textit{non-bank finance} and \textit{bank} sectors have significantly high degrees after 2008 in the outgoing information flow networks. We uncover how stock market turmoils affect the structure of the MSAs. Finally, we reveal the specificity of information source and sink sectors and make a conclusion that the root node sector as the information sink of the incoming information flow networks. Overall, our analyses show that the structure of information flow networks changes with time and the market exhibits a sector rotation phenomenon. Our work has important implications for market participants and policy makers in managing market risks and controlling the contagion of risks.
