City University of Hong Kong, Hong Kong.
Asia University, Taiwan, Lingnan University, Hong Kong.
Sci Rep. 2017 Feb 1;7:41379. doi: 10.1038/srep41379.
In this paper, we analyze the relationship among stock networks by focusing on the statistically reliable connectivity between financial time series, which accurately reflects the underlying pure stock structure. To do so, we firstly filter out the effect of market index on the correlations between paired stocks, and then take a t-test based P-threshold approach to lessening the complexity of the stock network based on the P values. We demonstrate the superiority of its performance in understanding network complexity by examining the Hong Kong stock market. By comparing with other filtering methods, we find that the P-threshold approach extracts purely and significantly correlated stock pairs, which reflect the well-defined hierarchical structure of the market. In analyzing the dynamic stock networks with fixed-size moving windows, our results show that three global financial crises, covered by the long-range time series, can be distinguishingly indicated from the network topological and evolutionary perspectives. In addition, we find that the assortativity coefficient can manifest the financial crises and therefore can serve as a good indicator of the financial market development.
在本文中，我们通过关注金融时间序列之间统计上可靠的连接性来分析股票网络之间的关系，这种连接性准确地反映了潜在的纯股票结构。为此，我们首先过滤掉市场指数对配对股票之间相关性的影响，然后基于 P 值采用基于 t 检验的 P 阈值方法来降低股票网络的复杂性。我们通过检验香港股票市场来证明其在理解网络复杂性方面的优越性。通过与其他过滤方法进行比较，我们发现 P 阈值方法提取了纯且显著相关的股票对，这些股票对反映了市场的明确分层结构。在使用固定大小移动窗口分析动态股票网络时，我们的结果表明，三个全球金融危机（涵盖了长程时间序列）可以从网络拓扑和演化角度明显区分开来。此外，我们发现配价系数可以体现金融危机，因此可以作为金融市场发展的良好指标。
