{
  "id": 3587,
  "url": "https://arxiv.org/abs/2605.28520v1",
  "title": "GS-FUSE: Granger-Supervised Gated Fusion and Multi-Granularity Alignment for Event-Driven Financial Forecasting",
  "summary": "Accurately forecasting the impact of salient financial events on markets is critical for investors and policymakers. However, existing multimodal time-series models typically fuse text and prices symmetrically, without an explicit way to decide when event text is truly predictive, and thus struggle to exploit the directional event-to-price structure and the heterogeneous roles of textual and price signals. In this work, we propose GS-Fuse, a multimodal event-based forecasting framework that empl",
  "authors": "Yang Zhang, En Chun, Ziyun Mao, Yulu Wu, Jun Wang",
  "category": "research",
  "topics": "safety-alignment,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-27T14:19:55.000Z",
  "fetched_at": "2026-07-14T16:30:23.244Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3587",
  "original_url": "https://arxiv.org/abs/2605.28520v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}