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Stock correlation analysis based on complex network

机译:基于复杂网络的股票相关性分析

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Aim at the study of the relationship of correlation between the stocks of SSE(Shanghai Stock Exchange) companies and the movement of SSE complex index, by using the mathematical statistics method and correlation coefficient analysis, select each stock's daily closing price as object, establish the complex network of stocks. Through preprocessing the data sets, we used R Studio get the mean correlation coefficient matrix, which is the foundation of the financial network. The results confirm the utility of the correlation coefficient analysis as a stability indicator for SSE stock market, since it proves that the correlation of stocks is different according to the crisis and prosperous of stock market in times. Its development is opposite to the market index, the interaction between the stocks attenuates when the complex index rises and enhances when the index falls. We also used K-means clustering algorithm to classify these nodes into different communities and found that the structures of networks vary widely according to their correlations. The method of this paper and the model it proposed is not only for the selection of our data sets, but also can be generalized to other fields of research.
机译:针对上海证券交易所(SSE)公司股票与上证综合指数变动之间的关系进行研究,运用数理统计方法和相关系数分析,选择每只股票的日收盘价为对象,建立沪深两市股票的日均收盘价。复杂的库存网络。通过对数据集进行预处理,我们使用R Studio获得了均值相关系数矩阵,这是金融网络的基础。该结果证明了相关系数分析作为上证所股票市场稳定指标的实用性,因为它证明了股票的相关性会根据危机和股市的繁荣而有所不同。它的发展与市场指数相反,当复杂指数上升时,股票之间的相互作用减弱,而当指数下降时,股票之间的相互作用增强。我们还使用K-means聚类算法将这些节点分为不同的社区,并发现网络的结构根据它们之间的相关性而有很大的不同。本文的方法及其提出的模型不仅用于选择我们的数据集,而且可以推广到其他研究领域。

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