Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2021

Abstract

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.

Keywords

Graph neural networks, transformers, attention mechanisms, timeseries forecasting, networks, multimodality, embeddings, finance

Discipline

Artificial Intelligence and Robotics | OS and Networks

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 2nd ACM International Conference on AI in Finance (ICAIF’21), Virtual Conference, 2021, November 3-5

First Page

1

Last Page

9

Identifier

10.1145/3490354.3494390

Publisher

ACM

City or Country

Virtual Conference

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