A Multimodal Event-Driven LSTM Model for Stock Prediction Using Online News

Qing Li, Jinghua Tan, Jun Wang, Hsinchun Chen

Research output: Contribution to journalArticlepeer-review

60 Scopus citations


In finance, it is believed that market information, namely, fundamentals and news information, affects stock movements. Such media-aware stock movements essentially comprise a multimodal problem. Two unique challenges arise in processing these multimodal data. First, information from one data mode will interact with information from other data modes. A common strategy is to concatenate various data modes into one compound vector; however, this strategy ignores the interactions among different modes. The second challenge is the heterogeneity of the data in terms of sampling time. Specifically, fundamental data consist of continuous values sampled at fixed time intervals, whereas news information emerges randomly. This heterogeneity can cause valuable information to be partially missing or can distort the feature spaces. In addition, the study of media-aware stock movements in previous work has focused on the one-to-one problem, in which it is assumed that news affects only the performance of the stocks mentioned in the reports. However, news articles also impact related stocks and cause stock co-movements. In this article, we propose a tensor-based event-driven LSTM model to address these challenges. Experiments performed on the China securities market demonstrate the superiority of the proposed approach over state-of-the-art algorithms, including AZFinText, eMAQT, and TeSIA.

Original languageEnglish (US)
Pages (from-to)3323-3337
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number10
StatePublished - Oct 1 2021


  • LSTM
  • Stock prediction
  • deep learning
  • multimodality
  • tensor

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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