Visual attention model for cross-sectional stock return prediction and end-to-end multimodal market representation learning

Ran Zhao, Yuntian Deng, Mark Dredze, Arun Verma, David Rosenberg, Amanda Stent

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Technical and fundamental analysis are traditional tools used to analyze stocks; however, the finance literature has shown that the price movement of each individual stock is highly correlated with that of other stocks, especially those within the same sector. In this paper we propose a generalpurpose market representation that incorporates fundamental and technical indicators and relationships between individual stocks. We treat the daily stock market as a 'market image' where rows (grouped by market sector) represent individual stocks and columns represent indicators. We apply a convolutional neural network over this market image to build market features in a hierarchical way. We use a recurrent neural network, with an attention mechanism over the market feature maps, to model temporal dynamics in the market. Our model outperforms strong baselines in both short-term and long-term stock return prediction tasks.We also show another use for our market image: To construct concise and dense market embeddings suitable for downstream prediction tasks.

Original languageEnglish (US)
Title of host publicationProceedings of the 32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019
EditorsRoman Bartak, Keith Brawner
PublisherThe AAAI Press
Pages98-103
Number of pages6
ISBN (Electronic)9781577358053
StatePublished - 2019
Event32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019 - Sarasota, United States
Duration: May 19 2019May 22 2019

Publication series

NameProceedings of the 32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019

Conference

Conference32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019
Country/TerritoryUnited States
CitySarasota
Period5/19/195/22/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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