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

arXiv: https://arxiv.org/abs/2510.04357 · HTML: https://mlsanity.com/paper/2510.04357/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 40 references that are in the map.
- First posted on arXiv 5 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [Hypergraph Convolution and Hypergraph Attention](https://mlsanity.com/paper/1901.08150/) (2019)
- [Low-Dimensional Hyperbolic Knowledge Graph Embeddings](https://mlsanity.com/paper/2005.00545/) (2020)
- [Graph Neural Networks for Graphs with Heterophily: A Survey](https://mlsanity.com/paper/2202.07082/) (2022)
- [Temporal Relational Ranking for Stock Prediction](https://mlsanity.com/paper/1809.09441/) (2018)
- [Meta-Transfer Learning for Low-Resource Abstractive Summarization](https://mlsanity.com/paper/2102.09397/) (2021)
- [Breaking Down Financial News Impact: A Novel AI Approach with Geometric Hypergraphs](https://mlsanity.com/paper/2409.00438/) (2024)
- [HSR: Hyperbolic Social Recommender](https://mlsanity.com/paper/2102.09389/) (2021)
- [Causal Spherical Hypergraph Networks for Modelling Social Uncertainty](https://mlsanity.com/paper/2506.17840/) (2025)
- [Actionable Interpretability via Causal Hypergraphs: Unravelling Batch Size Effects in Deep Learning](https://mlsanity.com/paper/2506.17826/) (2025)
- [ManifoldMind: Dynamic Hyperbolic Reasoning for Trustworthy Recommendations](https://mlsanity.com/paper/2507.02014/) (2025)
- [RicciFlowRec: A Geometric Root Cause Recommender Using Ricci Curvature on Financial Graphs](https://mlsanity.com/paper/2508.09334/) (2025)

## Built on by (most cited first, 2 of 2)

- [SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry](https://mlsanity.com/paper/2603.01168/) (2026)
- [Knowledge or Calculator? Decomposing the Skill Premium in Verifiable Financial Agent Workflows](https://mlsanity.com/paper/2610.03564/) (2026)

## Abstract (arXiv)

We propose the Causal Sphere Hypergraph Transformer (CSHT), a novel architecture for interpretable financial time-series forecasting that unifies Granger-causal hypergraph structure, Riemannian geometry, and 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 robust generalisation across market regimes and 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.