ST-Trader: A Spatial-Temporal Deep Neural Network for Modeling Stock Market Movement

Xiurui Hou, Kai Wang, Cheng Zhong, Zhi Wei

Research output: Contribution to journalArticlepeer-review

Abstract

Stocks that are fundamentally connected with each other tend to move together. Considering such common trends is believed to benefit stock movement forecasting tasks. However, such signals are not trivial to model because the connections among stocks are not physically presented and need to be estimated from volatile data. Motivated by this observation, we propose a framework that incorporates the inter-connection of firms to forecast stock prices. To effectively utilize a large set of fundamental features, we further design a novel pipeline. First, we use variational autoencoder (VAE) to reduce the dimension of stock fundamental information and then cluster stocks into a graph structure (fundamentally clustering). Second, a hybrid model of graph convolutional network and long-short term memory network (GCN-LSTM) with an adjacency graph matrix (learnt from VAE) is proposed for graph-structured stock market forecasting. Experiments on minute-level U.S. stock market data demonstrate that our model effectively captures both spatial and temporal signals and achieves superior improvement over baseline methods. The proposed model is promising for other applications in which there is a possible but hidden spatial dependency to improve time-series prediction.

Original languageEnglish (US)
Article number9395542
Pages (from-to)1015-1024
Number of pages10
JournalIEEE/CAA Journal of Automatica Sinica
Volume8
Issue number5
DOIs
StatePublished - May 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Artificial Intelligence

Keywords

  • Graph convolution network
  • long-short term memory network
  • stock market forecasting
  • variational autoencoder (VAE)

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