用图神经网络从股票因子中挖掘相关性，仅凭因子就达到顶尖预测效果。
GRU-PFG: Extract Inter-Stock Correlation from Stock Factors with Graph Neural Network
- 用图神经网络将股票因子映射为关系图，自动捕捉跨股票关联
- 在沪深300数据集上信息系数达0.134，超越传统因子模型
- 仅需股票因子输入，通用性强，适合实际部署场景
股票与行业复杂性给选股预测带来挑战。现有模型分为两类：一类如GRU和ALSTM，仅依赖股票因子，效果有限；另一类如HIST和TRA，引入行业信息、财务报告、舆论等多源数据，可捕捉股票间关联，但数据难以标准化且泛化性差。本文提出GRU-PFG（Project Factors into Graph）模型，仅以股票因子为输入，通过图神经网络提取股票间关联关系。实验表明，在CSI300数据集上，GRU-PFG的信息系数（IC）达0.134，优于HIST的0.131，显著超过GRU和Transformer，性能接近甚至媲美多源输入模型。该模型仅依赖股票因子，具备更强泛化潜力。
原文摘要 · Abstract (English)
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.
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