Tensor-Based Learning for Predicting Stock Movements
By Qing Li, LiLing Jiang, Ping Li, Hsinchun Chen. 2015
57 citations recorded by the source corpus.
Stock movements are essentially driven by new information. Market data, financial news, and social sentiment are believed to have impacts on stock markets. To study the correlation between information and stock movements, previous works typically concatenate the features of different information sources into one super feature vector. However, such concatenated vector approaches treat each information source separately and ignore their interactions. In this article, we model the multi-faceted investors’ information and their intrinsic links with tensors. To identify the nonlinear patterns between stock movements and new information, we propose a supervised tensor regression learning approach to investigate the joint impact of different information sources on stock markets. Experiments on CSI 100 stocks in the year 2011 show that our approach outperforms the state-of-the-art trading strategies.
Subfields: Electrical and Electronic Engineering, Finance, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
In Finance
In Management Science and Operations Research
Citations in the corpus
- Improving stock market prediction via heterogeneous information fusion (2017, methodology): “The closest approach to ours resides in [12, 13], where events and sentiments are also integrated in a tensor framework together with firm-specific features (e.g., P/B, P/E), to model the joint impacts on the stock market volatility.”
- Natural language based financial forecasting: a survey (2017, methodology): “This theory was proposed by [60], and supported by empirical evidence from [102], [66], and [65].”