An analysis of political turmoil effects on stock prices: A case study of US-China trade friction

Yukari Shirota, Kenji Yamaguchi, Akane Murakami, Michiya Morita

研究成果: 書籍の章/レポート/Proceedings会議への寄与査読

2 被引用数 (Scopus)

抄録

In the paper, we report an interesting result of changes of stock prices due to a political turmoil, the trade friction between China and US ignited in 2018, using the machine learning approach based on hierarchical clustering and Singular Value Decomposition methods and show such new approaches' possibilities and meaningfulness. The data we used are the top 100 global automobile manufactures' stock prices from 2018 to 2019 which were under the trade friction turmoil. The involved countries are Germany, Japan and US. One clear result is that the turmoil gave distinctively different effects on those countries' stock markets. We could identify three different clusters of stock price movements, that is, German, Japanese and US clusters. This result is expected to give some insights to the issue of international linkages between the movements of the markets' prices by adding a case of political turmoil.

本文言語英語
ホスト出版物のタイトルICAIF 2020 - 1st ACM International Conference on AI in Finance
出版社Association for Computing Machinery, Inc
ISBN(電子版)9781450375849
DOI
出版ステータス出版済み - 15 10月 2020
外部発表はい
イベント1st ACM International Conference on AI in Finance, ICAIF 2020 - Virtual, Online, 米国
継続期間: 15 10月 202016 10月 2020

出版物シリーズ

名前ICAIF 2020 - 1st ACM International Conference on AI in Finance

会議

会議1st ACM International Conference on AI in Finance, ICAIF 2020
国/地域米国
CityVirtual, Online
Period15/10/2016/10/20

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