From News to Returns: A Granger-Causal Hypergraph Transformer on the Sphere

2025-10-05

AI Summary

This work addresses the problem of modeling causal effects of financial news and sentiment data on asset returns while enabling interpretable time-series forecasting. To this end, we propose the first unified framework integrating Granger causality discovery, spherical Riemannian manifold embedding, and hypergraph neural networks: (i) a Granger causal hypergraph is constructed to capture dynamic, cross-market causal pathways; (ii) a spherical angular masking mechanism ensures geometric consistency and directional causal modeling on the unit sphere; and (iii) a causal-masked Transformer enhances robustness across heterogeneous market regimes. Experiments on the S&P 500 index (2018–2023, including the pandemic shock period) demonstrate significant improvements in return prediction accuracy, market regime classification, and top-asset ranking over state-of-the-art baselines. Our core contribution lies in unifying Granger causality, manifold learning, and hypergraph representation within a single, interpretable financial forecasting paradigm—marking the first such integration in the literature.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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&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.

Problem

• Modeling directional influence of news on asset returns
• Extracting Granger-causal dependencies in financial time-series
• Providing interpretable forecasting across different market regimes

Innovation

• Uses Granger-causal hypergraph structure for modeling
• Employs Riemannian geometry on hypersphere embeddings
• Implements causally masked Transformer attention mechanism