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Learning knowledge-enriched company embeddings for investment management
2021
Proceedings of the Second ACM International Conference on AI in Finance
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 Knowledge-Enriched Company Embedding (KECE), a novel multi-stage attentionbased dynamic network embedding model combining multimodal information of companies with knowledge from Wikipedia
doi:10.1145/3490354.3494390
fatcat:7ytwfhsgsvanpnre222orup4rm