Time-series Clustering of Global Automakers Stock Prices
Time-series Clustering of Global Automakers Stock Prices
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DOI：
10.52731/iee.v7.i2.626
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Y. Shirota;Akane Murakami
中科院分区：
文献类型：
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作者：
Y. Shirota;Akane Murakami
在本文中，我们描述了使用层次风险平价的层次聚类方法对股票价格数据进行分析。在金融工程中，聚类对于投资组合的开发是非常重要的。我们使用的数据是2018年至2019年全球百强行业的股价。行业领域是汽车制造业。这些行业的国家是日本、美国和德国。我们按顺序分析了这两年的两个月的数据。然后我们发现，当股价大幅下跌时，可能会明显出现以国家为基础的集群。在2018年美中国贸易摩擦引发的全球动荡时期，我们可以看出，日本集群和美国集群似乎界限明确。为了验证假设，我们追踪了这两年星系团的时间序列变化。结果，我们证明了假设是正确的，只要我们使用的是句号。此外，我们将最大损害期的基于国家的集群可视化为股价波动图和Markowitz的风险-收益图，以查看三个国家的趋势差异。
In the paper, we describe the stock price data analysis using the hierarchical clustering method named Hierarchical Risk Parity. In the financial engineering, clustering is important for the portfolio development. The data we used are the top 100 global industries' stock prices from 2018 to 2019. The industry field is automobile manufacturing. The countries of the industries are Japan, US and Germany. We analyzed sequentially a bi-month data for the two years. Then we found that when the stock price drastically decreased, there could clearly appear country-based clusters. In the global turmoil period by the US-China trade friction in 2018, we could identify that the Japan cluster and the US cluster appeared with clear boundaries. To verify the hypothesis, we traced the time series changes of the clusters through the two years. As a result, we proved the hypothesis was correct, as far as the period was what we used. In addition, we visualized the resultant country-based clusters of the largest damage period, as a stock price fluctuation plot and the Markowitz’s risk-return plot, to see the difference of the three countries’ trend.