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Graph Representation Learning for Similarity Stocks Analysis

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Abstract

Listed companies with similar or related fundamentals usually influence each other, and these influences are usually reflected in stock prices. For example, the momentum spillover effect in the behavioral finance theory describes the formation of lead-lag effects between the stock prices of related companies. The relationship between listed companies consists of many types, such as relationships in the industry chain, industry information, transaction information, patent sharing degree, equity, etc. We construct a set of industry chain knowledge graph of listed companies to describe the production and supply relationship between the upstream and downstream of listed companies. Then, graph representation learning method is used to study the relevance between listed company entities in the knowledge graph. It includes dimensions such as industry and transaction information of listed companies as weights to optimize the graph representation learning process, and finally calculates the similarity index between listed companies. To evaluate the effectiveness of the method, we conduct a link prediction experiment and construct a stock quantitative investment portfolio based on the similarity index. The result of the quantitative backtest experiment based on China’s stock market data in the last 10 years shows that the graph representation learning method we proposed can be used to study the momentum spillover effect and obtain investment returns.

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Acknowledgements

This work was partially supported by the Technology and Innovation Major Project of the Ministry of Science and Technology of China under Grant 2020AAA0108400 and 2020AAA0108403, and partially supported by the Beijing Natural Science Foundation (Z210002). Portions of this work were presented at the 2021 IEEE 6th International Conference on Smart Cloud (SmartCloud) in 2021, Similarity Analysis of Knowledge Graph-based Company Embedding for Stocks Portfolio.

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Authors and Affiliations

  1. Computer Network Information Center, Chinese Academy of Sciences, Beijing, China

    Boyao Zhang, Zongguo Wang, Yonghua Zhao & Yangang Wang

  2. University of Chinese Academy of Sciences, Beijing, China

    Boyao Zhang

  3. Beijing University of Aeronautics and Astronautics, College of Software, Beijing, China

    Chao Yang, Jingqi Sun & Lihua Wang

  4. China Internet Network Information Center, Beijing, China

    Haikuo Zhang

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  1. Boyao Zhang
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Correspondence to Haikuo Zhang.

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Zhang, B., Yang, C., Zhang, H. et al. Graph Representation Learning for Similarity Stocks Analysis. J Sign Process Syst 94, 1283–1292 (2022). https://doi.org/10.1007/s11265-022-01755-6

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