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

Key Points

• Time-series forecasting reveals potential early warnings of systemic risk, benefiting both investors and regulators.
• Findings indicate that language-informed graph forecasting can provide economically interpretable insights.
• Analysis focused on financial network indicators and the implications for market surveillance practices.
• Highlights the broader potential for enhancing policy design using advanced forecasting methodologies.

Abstract