From News to Returns: A Granger-Causal Hypergraph Transformer on the Sphere
This work addresses the problem of interpretable and robust financial forecasting for investors and analysts, though it appears incremental as it combines existing concepts like Granger causality and Transformers in a novel way.
The paper tackled financial time-series forecasting by modeling the influence of news and sentiment on asset returns, achieving consistent outperformance over baselines in return prediction, regime classification, and top-asset ranking tasks on S&P 500 data from 2018 to 2023, including during the 2020 COVID-19 shock.
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.