H3M-SSMoEs：结合LLM推理与风格结构混合专家的基于超图的多模态学习
H3M-SSMoEs: Hypergraph-based Multimodal Learning with LLM Reasoning and Style-Structured Mixture of Experts
AI总结：
针对股票走势预测的多模态与动态关系建模难题，提出结合超图、LLM推理与风格结构混合专家的H3M-SSMoEs架构，在三大股市实验中精度与投资表现均超SOTA且风控有效。
摘要：
由于复杂的时间依赖、异构模态以及动态演化的股票间关系，股票走势预测从根本上仍具挑战性。现有方法往往无法在可扩展框架中统一结构建模、语义建模与市场状态自适应建模。本文提出H3M-SSMoEs，这是一种结合大语言模型（LLM）推理与风格结构混合专家的新型基于超图的多模态架构，包含三项核心创新：（1）多上下文多模态超图，通过局部上下文超图（LCH）分层捕获细粒度时空动态，通过全局上下文超图（GCH）捕捉持久的股票间依赖关系，采用共享跨模态超边与Jensen-Shannon散度加权机制实现自适应关系学习与跨模态对齐；（2）LLM增强推理模块，利用带有轻量适配器的冻结大语言模型，对量化模态与文本模态进行语义融合与对齐，借助金融领域专属知识丰富表征；（3）风格结构混合专家（SSMoEs），融合共享市场专家与行业专属专家，每个专家由可学习风格向量参数化，可在稀疏激活下实现市场状态感知的专业化适配。在三大主流股票市场上开展的大量实验表明，H3M-SSMoEs在预测精度与投资表现上均超越现有最优方法，同时具备有效的风险控制能力。数据集、源代码与模型权重可在我们的GitHub仓库获取：https://github.com/PeilinTime/H3M-SSMoEs。
英文摘要：
Stock movement prediction remains fundamentally challenging due to complex temporal dependencies, heterogeneous modalities, and dynamically evolving inter-stock relationships. Existing approaches often fail to unify structural, semantic, and regime-adaptive modeling within a scalable framework. This work introduces H3M-SSMoEs, a novel Hypergraph-based MultiModal architecture with LLM reasoning and Style-Structured Mixture of Experts, integrating three key innovations: (1) a Multi-Context Multimodal Hypergraph that hierarchically captures fine-grained spatiotemporal dynamics via a Local Context Hypergraph (LCH) and persistent inter-stock dependencies through a Global Context Hypergraph (GCH), employing shared cross-modal hyperedges and Jensen-Shannon Divergence weighting mechanism for adaptive relational learning and cross-modal alignment; (2) a LLM-enhanced reasoning module, which leverages a frozen large language model with lightweight adapters to semantically fuse and align quantitative and textual modalities, enriching representations with domain-specific financial knowledge; and (3) a Style-Structured Mixture of Experts (SSMoEs) that combines shared market experts and industry-specialized experts, each parameterized by learnable style vectors enabling regime-aware specialization under sparse activation. Extensive experiments on three major stock markets demonstrate that H3M-SSMoEs surpasses state-of-the-art methods in both superior predictive accuracy and investment performance, while exhibiting effective risk control. Datasets, source code, and model weights are available at our GitHub repository: https://github.com/PeilinTime/H3M-SSMoEs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
