Keywords
Graph Neural Networks
Abstract
Stock trend prediction has long been a central problem in computational finance, attracting substantial research interest due to its profound implications for investment strategy, risk management, and market understanding. Recent advances in Graph Neural Networks (GNNs) have demonstrated promising capabilities in capturing relational structures among financial entities, with the Stock State Space Graph (S³G) model representing a notable contribution that leverages state space modeling within graph architectures for enhanced stock trend prediction. Despite these advances, existing GNN-based approaches predominantly rely on historical price and volume data, largely overlooking the rich informational value embedded in textual sources such as financial news, social media discussions, and analyst reports. This paper proposes Sentiment-Enhanced Temporal Graph Neural Network (SET-GNN), a novel framework that integrates sentiment analysis derived from financial news and social media with temporal graph neural network architectures to improve stock trend prediction accuracy and robustness. Our methodology incorporates a dual-branch architecture: one branch processes structured market data through temporal graph convolutions, while the other branch extracts sentiment embeddings from textual data using transformer-based encoders. These branch outputs are fused through a cross-attention mechanism that dynamically weights sentiment signals relative to market structure signals over time. Extensive experiments conducted on S&P 500 constituent stocks demonstrate that SET-GNN significantly outperforms both traditional machine learning baselines and state-of-the-art GNN-only approaches, achieving an accuracy improvement of 3.7% over the S³G baseline and a reduction in directional prediction error of 12.4%. Ablation studies further confirm the marginal contribution of sentiment integration across different market regimes, with particularly pronounced improvements observed during periods of elevated market volatility. Our findings underscore the importance of multimodal information fusion in financial prediction tasks and establish a new benchmark for sentiment-aware stock trend forecasting.
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