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.

翻译：准确预测股票走势对投资策略至关重要。股票价格受多种形式信息的影响，包括财务指标、情感分析、新闻文本和关系结构。然而，主流分析方法往往仅处理单模态或双模态数据源，忽视了多模态数据的复杂性。进一步使问题复杂化的是数据稀疏性以及模态间语义冲突的问题，现有模型常忽略这些因素，导致性能不稳定并限制实际应用。为克服这些不足，本研究提出一种新颖架构——基于门控交叉注意力机制的多模态稳定融合模型，旨在稳健整合多模态输入以预测股票走势。该框架包含三个核心组件：(1) 三模态编码模块，负责处理指标序列、动态文档和关系图，并标准化其特征表示；(2) 跨特征融合模块，通过一对门控交叉注意力网络，使主要特征和一致性特征引导三种模态的多模态融合；(3) 预测模块，通过时序和维度缩减优化融合特征，以执行精确的走势预测。实证评估表明，该框架超越了当前主流方法，在四个多模态数据集上分别实现了8.1%、6.1%、21.7%和31.6%的性能提升，这归因于其增强的多模态融合稳定性。