基于复杂网络的股票社团化分析

Stock Community Analysis Based on Complex Network

为了更清楚地了解股票间价格波动的相互影响,利用改进的Newman贪婪算法将沪市A股成功分为13个社团,并根据其紧密程度,得到内部股价波动影响关系比较敏感的社团。另外,根据股票间的吸引率对社团之间的影响程度进行了量化,找到联系最紧密的两个社团。从社团结构可以读出大量的市场信息,为投资决策以及评定行业前景提供可靠的依据,同时也体现出复杂网络的应用价值。

In order to achieve a clearer understanding on stock price volatility under the mutual influence,an improved greedy algorithm was employed to Shanghai Stock Market,which was divided into 13 communities.According to their closeness degree of relationship between fluctuations in stock price within a society,the most sensitive to price fluctuations community was obtained.The attracting rate of the various stocks was quantified according to the influential degree on the communities.Two most closely communities were figured out.From the community structure,investors could read out a lot of market information.It is not only helpful for decision-making and assessment of the prospects for the industry,but also helpful for strengthening the stock complex network community-oriented application.