Learning knowledge-enriched company embeddings for investment management

• Main Authors: ANG, Gary, LIM, Ee-peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Graph neural networks; transformers; attention mechanisms; timeseries forecasting; networks; multimodality; embeddings; finance; Artificial Intelligence and Robotics; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/6650; https://ink.library.smu.edu.sg/context/sis_research/article/7653/viewcontent/KECE_ICAIF21.pdf
• Institution: Singapore Management University

Relationships between companies serve as key channels through which the effects of past stock price movements and news events propagate and influence future price movements. Such relationships can be implicitly found in knowledge bases or explicitly represented as knowledge graphs. In this paper, we propose KnowledgeEnriched Company Embedding (KECE), a novel multi-stage attentionbased dynamic network embedding model combining multimodal information of companies with knowledge from Wikipedia and knowledge graph relationships from Wikidata to generate company entity embeddings that can be applied to a variety of downstream investment management tasks. Experiments on an extensive set of real-world stock prices and news datasets show that the proposed KECE model outperforms other state-of-the-art models on key investment management tasks.