GRU-PFG: Extract Inter-Stock Correlation from Stock Factors with Graph Neural Network
This addresses stock prediction for investors by offering a model with comparable performance to more complex methods but using only standardized stock factors, potentially improving generalization.
The paper tackles stock prediction by proposing GRU-PFG, a model that uses only stock factors as input and extracts inter-stock correlations with graph neural networks, achieving an IC of 0.134 on the CSI300 dataset, outperforming HIST (0.131) and significantly surpassing GRU and Transformer.
The complexity of stocks and industries presents challenges for stock prediction. Currently, stock prediction models can be divided into two categories. One category, represented by GRU and ALSTM, relies solely on stock factors for prediction, with limited effectiveness. The other category, represented by HIST and TRA, incorporates not only stock factors but also industry information, industry financial reports, public sentiment, and other inputs for prediction. The second category of models can capture correlations between stocks by introducing additional information, but the extra data is difficult to standardize and generalize. Considering the current state and limitations of these two types of models, this paper proposes the GRU-PFG (Project Factors into Graph) model. This model only takes stock factors as input and extracts inter-stock correlations using graph neural networks. It achieves prediction results that not only outperform the others models relies solely on stock factors, but also achieve comparable performance to the second category models. The experimental results show that on the CSI300 dataset, the IC of GRU-PFG is 0.134, outperforming HIST's 0.131 and significantly surpassing GRU and Transformer, achieving results better than the second category models. Moreover as a model that relies solely on stock factors, it has greater potential for generalization.