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基于图的熵和域间联动的金融市场解释性变化检测

Explanatory Change Detection in Financial Markets by Graph-Based Entropy and Inter-Domain Linkage.

作者信息

Nishikawa Yosuke, Yoshino Takaaki, Sugie Toshiaki, Nakata Yoshiyuki, Itou Kakeru, Ohsawa Yukio

机构信息

Department of Systems Innovation, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.

Nissay Asset Management Corporation, Marunouchi Building, 1-6-6, Marunouchi, Chiyoda-ku, Tokyo 100-8219, Japan.

出版信息

Entropy (Basel). 2022 Nov 25;24(12):1726. doi: 10.3390/e24121726.

Abstract

In this study, we analyzed structural changes in financial markets under COVID-19 to support investors' investment decisions. Because an explanation of these changes is necessary to respond appropriately to said changes and prepare for similar major changes in the future, we visualized the financial market as a graph. The hypothesis was based on expertise in the financial market, and the graph was analyzed from a detailed perspective by dividing the graph into domains. We also designed an original change-detection indicator based on the structure of the graph. The results showed that the original indicator was more effective than the comparison method in terms of both the speed of response and accuracy. Explanatory change detection of this method using graphs and domains allowed investors to consider specific strategies.

摘要

在本研究中,我们分析了新冠疫情下金融市场的结构变化,以支持投资者的投资决策。由于有必要对这些变化作出解释,以便对上述变化作出适当反应并为未来类似的重大变化做好准备,我们将金融市场可视化为一张图表。该假设基于金融市场方面的专业知识,并且通过将图表划分为不同领域,从详细的角度对图表进行了分析。我们还基于图表结构设计了一个原创的变化检测指标。结果表明,就响应速度和准确性而言,该原创指标比比较方法更有效。使用图表和领域对这种方法进行解释性变化检测,使投资者能够考虑具体策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c81b/9778065/dc940ee1ba77/entropy-24-01726-g001.jpg

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