Classification of Japanese Electrical Equipment Manufacturing Industry Recovery Patterns after Disasters: Case Study of August 2019

K. Yamaguchi, Y. Shirota
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Abstract

In the paper, we analyze the recovery pattern of Japanese electrical equipment manufacturing companies after the President Trump remark in August 2019. The President’s remark made the companies’ stock prices decreased severely. The research consists of two parts. In the first part, we conducted Random Matrix Theory to extract representative decline/recovery patterns. Then we tagged A/B/C/D to the companies’ recovery types. The class A means a strong recover power. Then as the second part, we conducted machine learning tree-based classification using the tags A/B/C. The predictors are eight variables like ROA, ROE, and VAR. The resultant Decision Tree model provided us with the two different approaches to the class A group. The recovery and repulsion power will be higher in the company with high ROA and in the company that manufactured the product with high VAR. In addition, another class A company group is made and the feature is the high inventory turnover ratio.
日本电气设备制造业灾后恢复模式分类——以2019年8月为例
本文分析了2019年8月特朗普总统发表言论后,日本电气设备制造企业的复苏模式。总统的话使这些公司的股票价格大幅下跌。本研究分为两部分。在第一部分中,我们运用随机矩阵理论提取有代表性的下降/恢复模式。然后我们将A/B/C/D标记为公司的复苏类型。A级意味着强大的恢复能力。然后作为第二部分,我们使用标签A/B/C进行了基于机器学习树的分类。预测因子是8个变量,如ROA、ROE和VAR。最终的决策树模型为我们提供了两种不同的方法来分析A类群体。在ROA高的公司和生产产品VAR高的公司中,回收力和斥力会更高。另外,又组成了一个A类公司集团,其特点是存货周转率高。
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