A Tensor-Based Information Framework for Predicting the Stock Market

Qing Li, Yuanzhu Chen, Li Ling Jiang, Ping Li, Hsinchun Chen

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


To study the influence of information on the behavior of stock markets, a common strategy in previous studies has been to concatenate the features of various information sources into one compound feature vector, a procedure thatmakes it more difficult to distinguish the effects of different information sources.We maintain that capturing the intrinsic relations among multiple information sources is important for predicting stock trends. The challenge lies in modeling the complex space of various sources and types of information and studying the effects of this information on stock market behavior. For this purpose, we introduce a tensorbased information framework to predict stock movements. Specifically, our framework models the complex investor information environment with tensors. A global dimensionality-reduction algorithm is used to capture the links among various information sources in a tensor, and a sequence of tensors is used to represent information gathered over time. Finally, a tensor-based predictive model to forecast stock movements, which is in essence a high-order tensor regression learning problem, is presented. Experiments performed on an entire year of data for China Securities Index stocks demonstrate that a trading system based on our framework outperforms the classic Top-N trading strategy and two state-of-the-art media-aware trading.

Original languageEnglish (US)
Article number11
JournalACM Transactions on Information Systems
Issue number2
StatePublished - Feb 2016


  • News
  • Predictive model
  • Social media
  • Stock
  • Tensor
  • Trading strategy

ASJC Scopus subject areas

  • Information Systems
  • Business, Management and Accounting(all)
  • Computer Science Applications


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