Using external knowledge for financial event prediction based on graph neural networks

  • Yiying Yang
  • , Zhongyu Wei*
  • , Qin Chen
  • , Libo Wu
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

46 Scopus citations

Abstract

This paper focuses on a novel financial event prediction task that takes a historical event chain as input and predicts what event will happen next. We introduce financial news as supplementary information to solve problems of multiple interpretations of same financial event. Besides, a gated graph neural network based approach is utilized to capture complicated relationships between event graphs for better event prediction. For the evaluation, we build a new dataset consisting of financial events for thousands of Chinese listed companies from 2013 to 2017. Experimental results show the effectiveness of our proposed model.

Original languageEnglish
Title of host publicationCIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2161-2164
Number of pages4
ISBN (Electronic)9781450369763
DOIs
StatePublished - 3 Nov 2019
Externally publishedYes
Event28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China
Duration: 3 Nov 20197 Nov 2019

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference28th ACM International Conference on Information and Knowledge Management, CIKM 2019
Country/TerritoryChina
CityBeijing
Period3/11/197/11/19

Keywords

  • Event Prediction
  • Event Representation
  • Graph Neural Network

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