Time-series Clustering of Global Automakers Stock Prices
Abstract
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.
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