{"ID":2860408,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.04357","arxiv_id":"2510.04357","title":"From News to Returns: A Granger-Causal Hypergraph Transformer on the Sphere","abstract":"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\\\u0026P 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.","short_abstract":"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 n...","url_abs":"https://arxiv.org/abs/2510.04357","url_pdf":"https://arxiv.org/pdf/2510.04357v1","authors":"[\"Anoushka Harit\",\"Zhongtian Sun\",\"Jongmin Yu\"]","published":"2025-10-05T20:51:59Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"q-fin.CP\"]","methods":"[\"Transformer\"]","has_code":false}