Constructing Knowledge Graph for Financial Securities and Discovering Related Stocks with Knowledge Association

[Objective] This paper constructs domain knowledge graph based on knowledge association and discovers industry characteristics and related stocks, aiming to improve investors’ decision making. [Methods] Firstly, we constructed the “seed” knowledge graph with stock data. Then, we conducted entity extraction and relationship classification with unstructured text data based on FinBERT pre-training model to generate the triples. Third, we merged the seed graph and the triples to create the knowledge graph for financial securities. Fourth, based on the graph, link prediction, similarity calculation and other data mining algorithms, we discovered the related stocks and their hidden characteristics. Our findings were preliminarily verified by statistical methods. [Results] Our new knowledge graph was constructed with 111, 845 entities and 163, 370 relationships. We analyzed 10 cross-industry stocks having the highest similarity with “Northeast Securities”. We also examined the potential nonlinear correlation between stocks using “Sihuan Biology”. [Limitations] The constructed knowledge graph only included the impacts of static information (e. g., industry and shareholder ownership) on stock correlation. [Conclusions] Our new knowledge graph provides strong data analytics support for investors to make effective portfolio strategies and predict stock trends. © 2022, Chinese Academy of Sciences. All rights reserved.