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Volume 8 Issue 5
May  2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2021  >  8(5): 1015-1024
X. R. Hou, K. Wang, C. Zhong, and Z. Wei, "ST-Trader: A Spatial-Temporal Deep Neural Network for Modeling Stock Market Movement," IEEE/CAA J. Autom. Sinica, vol. 8, no. 5, pp. 1015-1024, May. 2021. doi: 10.1109/JAS.2021.1003976
Citation: X. R. Hou, K. Wang, C. Zhong, and Z. Wei, "ST-Trader: A Spatial-Temporal Deep Neural Network for Modeling Stock Market Movement," IEEE/CAA J. Autom. Sinica, vol. 8, no. 5, pp. 1015-1024, May. 2021. doi: 10.1109/JAS.2021.1003976
shu

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

doi: 10.1109/JAS.2021.1003976
  • 1.

    Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102 USA

  • 2.

    School of Business, Stevens Institute of Technology, Hoboken, NJ 07030 USA

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    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.

     

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    • 1 https://en.wikipedia.org/wiki/Efficient-market_hypothesis
    2 https://en.wikipedia.org/wiki/2010_flash_crash.
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    Highlights

    • Learn latent features of companies’ fundamental variables via Variational Autoencoder
    • Model graph structured interaction among stocks and price fluctuations simultaneously
    • Experiments on both predicting numerical stock price and binary stock price movement

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