GS-FUSE: Granger-Supervised Gated Fusion and Multi-Granularity Alignment for Event-Driven Financial Forecasting
arXiv:2605.28520v1 Announce Type: new Abstract: 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 employs (i) a Granger-supervised, causal-aware gated fus
The proliferation of advanced AI models demands more sophisticated financial forecasting tools that can untangle causal relationships from event data, rather than mere correlations.
This development allows investors and policymakers to more accurately predict market responses to specific events, moving beyond symmetrical data fusion to a causal-aware approach.
Financial forecasting models will evolve from simply processing multimodal data to explicitly identifying and leveraging the directional impact of textual events on price movements.
- · Quantitative Investors
- · Financial AI Researchers
- · Hedge Funds
- · Financial Data Providers
- · Traditional Econometric Models
- · Purely Correlation-Based Trading Strategies
More precise and explainable event-driven financial predictions become possible.
Increased adoption of Granger-causality principles in AI-driven financial models could lead to new avenues for market manipulation or increased regulatory scrutiny.
Markets might become more efficient in pricing in new information, potentially reducing arbitrage opportunities for human traders over time.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI