These findings demonstrate that LLM-driven time-series forecasting can provide early warnings of systemic risk and generate economically interpretable insights for investors and regulators. The results highlight the broader potential of language-informed graph forecasting as a new paradigm for financial market surveillance and policy design.
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Wang et al. (Thu,) studied this question.
www.synapsesocial.com/papers/69a75bc7c6e9836116a23be3 — DOI: https://doi.org/10.3389/frai.2026.1722121
Mini Han Wang
Ying Yeung
Frontiers in Artificial Intelligence
Chinese University of Hong Kong
Institute of Computing Technology
Shenzhen Polytechnic
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