A Knowledge Graph Based Framework in Relationship Modelling and Real-time Monitoring of Market Participants. (November 2020)

• Record Type: Journal Article
• Authors: Cheng, Wenliang; Guo, Kaihao; Jiang, Tianpei; Zhang, Haipeng
• Is Part Of: Journal of physics. Volume 1682(2020)
• Publication Date: 2020-11
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1682/1/012026
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Various types of relationships among participants exist in the foreign exchange market, such as transaction, credit relations inside the market and subcompany, investing and shareholding relations outside the market. In the market with complex relationships, an incident happened in one institution may have "butterfly effect". To monitor systemic financial risks, we integrate various types of data and establish a Knowledge Graph describing the relationships between market participants. Based on the Knowledge Graph, we design a framework to measure the impact of various events on the entire market by connecting the internal market relationships to external market related incidents. By using the BiLSTM-CRF model, we extract the relevant entities in the market news with high accuracy and measure the impact on the market with graph related indicators such as closeness and betweenness centralities. In addition, to use external unstructured data, we use a document-level event extraction method to analyse these unstructured datasets and extract new events and relationships from them to dynamically update the Knowledge Graph.