MASTER: Market-Guided Stock Transformer for Stock Price Forecasting


  • Tong Li Shanghai Jiao Tong University
  • Zhaoyang Liu Alibaba Group
  • Yanyan Shen Shanghai Jiao Tong University
  • Xue Wang Alibaba Group
  • Haokun Chen Alibaba Group
  • Sen Huang Alibaba Group



APP: Other Applications, DMKM: Mining of Spatial, Temporal or Spatio-Temporal Data


Stock price forecasting has remained an extremely challenging problem for many decades due to the high volatility of the stock market. Recent efforts have been devoted to modeling complex stock correlations toward joint stock price forecasting. Existing works share a common neural architecture that learns temporal patterns from individual stock series and then mixes up temporal representations to establish stock correlations. However, they only consider time-aligned stock correlations stemming from all the input stock features, which suffer from two limitations. First, stock correlations often occur momentarily and in a cross-time manner. Second, the feature effectiveness is dynamic with market variation, which affects both the stock sequential patterns and their correlations. To address the limitations, this paper introduces MASTER, a MArkert-guided Stock TransformER, which models the momentary and cross-time stock correlation and leverages market information for automatic feature selection. MASTER elegantly tackles the complex stock correlation by alternatively engaging in intra-stock and inter-stock information aggregation. Experiments show the superiority of MASTER compared with previous works and visualize the captured realistic stock correlation to provide valuable insights.



How to Cite

Li, T., Liu, Z., Shen, Y., Wang, X., Chen, H., & Huang, S. (2024). MASTER: Market-Guided Stock Transformer for Stock Price Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 38(1), 162-170.



AAAI Technical Track on Application Domains