用几何结构建模新闻对股市影响，提升预测透明度与鲁棒性。
From News to Returns: A Granger-Causal Hypergraph Transformer on the Sphere
- 将新闻因果关系编码为球面上的有向超边，结合几何约束注意力机制。
- 在2018–2023年标普500数据上，多任务表现优于基线模型。
- 适合关注可解释金融预测与极端市场下稳定性的研究者。
我们提出因果球面超图变压器（CSHT），一种用于可解释金融时序预测的新架构，融合了格兰杰因果超图结构、黎曼几何与因果掩码Transformer注意力。CSHT通过提取多变量格兰杰因果依赖，将金融新闻和情绪对资产收益的定向影响编码为球面超图上的有向超边，并利用角度掩码约束注意力，保持时间方向性和几何一致性。在2018至2023年标普500数据（包含2020年新冠疫情冲击）上评估，CSHT在收益预测、市场状态分类和前五资产排序任务中均持续优于基线。通过强制预测因果结构并将在黎曼流形中嵌入变量，CSHT实现了跨市场状态的鲁棒泛化与从宏观经济事件到个股响应的透明归因路径。结果表明，该方法是不确定性下可信金融预测的原理性且实用的解决方案。
原文摘要 · Abstract (English)
We propose the Causal Sphere Hypergraph Transformer (CSHT), a novel architecture for interpretable financial time-series forecasting that unifies \emph{Granger-causal hypergraph structure}, \emph{Riemannian geometry}, and \emph{causally masked Transformer attention}. CSHT models the directional influence of financial news and sentiment on asset returns by extracting multivariate Granger-causal dependencies, which are encoded as directional hyperedges on the surface of a hypersphere. Attention is constrained via angular masks that preserve both temporal directionality and geometric consistency. Evaluated on S\&P 500 data from 2018 to 2023, including the 2020 COVID-19 shock, CSHT consistently outperforms baselines across return prediction, regime classification, and top-asset ranking tasks. By enforcing predictive causal structure and embedding variables in a Riemannian manifold, CSHT delivers both \emph{robust generalisation across market regimes} and \emph{transparent attribution pathways} from macroeconomic events to stock-level responses. These results suggest that CSHT is a principled and practical solution for trustworthy financial forecasting under uncertainty.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。