GRU-PFG: Extract Inter-Stock Correlation from Stock Factors with Graph Neural Network
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.
Code (1)
Tasks
Graph Neural NetworkPredictionStock PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Working Paper: Improved Stock Price Forecasting Algorithm based on Feature-weighed Support Vector Regression by using Grey Correlation Degree
With the widespread engineering applications ranging from artificial intelligence and big data decision-making, originally a lot of tedious financial data processing, processing and analysis have become more and more con…
Decision MakingregressionStock PredictionModelling stock correlations with expected returns from investors
Stock correlations is crucial to asset pricing, investor decision-making, and financial risk regulations. However, microscopic explanation based on agent-based modeling is still lacking. We here propose a model derived f…
Decision MakingDeep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations
In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task. Stock forecasting is complex, given the stochastic dynamics and non-stationary …
Decision MakingGraph Neural NetworkStock Market PredictionMultimodal Deep Learning for Finance: Integrating and Forecasting International Stock Markets
In today's increasingly international economy, return and volatility spillover effects across international equity markets are major macroeconomic drivers of stock dynamics. Thus, information regarding foreign markets is…
Multimodal Deep LearningPredictionStock PredictionLLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction
Recently, Large Language Models (LLMs) have attracted significant attention for their exceptional performance across a broad range of tasks, particularly in text analysis. However, the finance sector presents a distinct …
Sentiment AnalysisTime SeriesTime Series Forecasting