Large language model-driven time-series forecasting of financial network indicators

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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Publication Details

Journal
Frontiers in Artificial Intelligence
Published
2026-01-29
DOI
https://doi.org/10.3389/frai.2026.1722121
Citations
1
Primary Topic
Stock Market Forecasting Methods
Type
article
Field-Weighted Citation Impact
58.08

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article

Large language model-driven time-series forecasting of financial network indicators

Ying Yeung, Mini Han Wang
1 citations
Frontiers in Artificial Intelligence
Stock Market Forecasting Methods
58.08
article

Large language model-driven time-series forecasting of financial network indicators

Ying Yeung, Mini Han Wang
article en
1 citations

Abstract

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.

Frontiers in Artificial IntelligenceVol. 9
Chinese University of Hong Kong (HK), Shenzhen Polytechnic University (CN), Institute of Computing Technology (CN), The Polytechnic University of Japan (JP), Zhuhai Institute of Advanced Technology (CN)
National Natural Science Foundation of China, National Office for Philosophy and Social Sciences
Openalex Percentile: Top 1%
Stock Market Forecasting Methods
58.08
DOIOpen Access PDFOpenAlex
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