Long-term Time Series Data Clustering of Stock Prices for Portfolio Selection
Long-term Time Series Data Clustering of Stock Prices for Portfolio Selection
复制标题
用于投资组合选择的股票价格长期时间序列数据聚类
DOI：
10.1109/soli54607.2021.9672407
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Murakami Akane
中科院分区：
文献类型：
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作者：
Shirota Yukari;Murakami Akane
利用DTW距离测度，采用k-Shape和k-means两种聚类方法对股票数据进行聚类，并对聚类结果进行了比较。该数据是2018年至2020年全球129家电子制造商的股票价格，其中包括2018年最糟糕的圣诞节和COVID-19爆发的开始。涉及的国家有美国、中国、台湾、韩国、日本和其他一些国家。k-Shape的聚类结果显示，COVID-19动荡对这些国家的股市产生了明显不同的影响。聚类的模式可以被可视化以识别聚类之间的差异。我们发现八个集群中的每个集群都由相同的国家公司组成。由此，我们可以猜测，投资者或他们的算法倾向于根据国家而不是单个公司的表现来投资公司。
Clustering for stock data is conducted with two clustering methods, k-Shape and k-means with DTW distance measure and the results are compared. The data is the top 129 global electronics manufactures' stock prices from 2018 to 2020 which included the worst Christmas in 2018 and the beginning of COVID-19 outbreak. The involved countries are US, China, Taiwan, Korea, Japan and some others. The clustering results by k-Shape indicate distinctively different effects on those countries' stock markets due to the COVID-19 turmoil. The patterns of the clusters can be visualized to identify the differences among the clusters. We found that each of eight clusters comprises of the same country companies. From that, we could guess that investors or their algorithms tend to invest in companies according to its country rather than the individual company's performance.