The accurate prediction of stock movements is crucial for investment
strategies. Stock prices are subject to the influence of various forms of
information, including financial indicators, sentiment analysis, news
documents, and relational structures. Predominant analytical approaches,
however, tend to address only unimodal or bimodal sources, neglecting the
complexity of multimodal data. Further complicating the landscape are the
issues of data sparsity and semantic conflicts between these modalities, which
are frequently overlooked by current models, leading to unstable performance
and limiting practical applicability. To address these shortcomings, this study
introduces a novel architecture, named Multimodal Stable Fusion with Gated
Cross-Attention (MSGCA), designed to robustly integrate multimodal input for
stock movement prediction. The MSGCA framework consists of three integral
components: (1) a trimodal encoding module, responsible for processing
indicator sequences, dynamic documents, and a relational graph, and
standardizing their feature representations; (2) a cross-feature fusion module,
where primary and consistent features guide the multimodal fusion of the three
modalities via a pair of gated cross-attention networks; and (3) a prediction
module, which refines the fused features through temporal and dimensional
reduction to execute precise movement forecasting. Empirical evaluations
demonstrate that the MSGCA framework exceeds current leading methods, achieving
performance gains of 8.1%, 6.1%, 21.7% and 31.6% on four multimodal datasets,
respectively, attributed to its enhanced multimodal fusion stability.

通过提出一种名为 MSGCA 的新型架构，能够处理股票预测中的多模态数据，实现多模态融合和稳定性，取得了相对于其他模型的各个方面的优势。