用大模型实时监控股债汇三类资产风险，提升决策准确性。
Cross-Asset Risk Management: Integrating LLMs for Real-Time Monitoring of Equity, Fixed Income, and Currency Markets
- 融合多源数据，用大模型分析金融文本与市场报告。
- 回测显示预测市场波动比传统方法更准确。
- 适合需要实时风险监控的金融机构参考。
大型语言模型（LLMs）在金融领域，尤其是跨资产类别的风险管理中展现出强大潜力。本文提出一种跨资产风险管理体系，利用LLMs实现对股票、固定收益和货币市场的实时监控。该框架通过整合多样化数据源，动态评估风险，显著提升决策效率。模型能有效合成并分析市场信号，识别潜在风险与机遇，并提供资产类别间的全景视图。借助先进分析技术，我们利用LLMs解读财务文本、新闻文章及市场报告，使风险判断融入更广泛的市场语境。大量回测与实时仿真验证了该框架的有效性，结果显示其在预测市场变化方面优于传统方法。对实时数据的集成增强了系统响应能力，使金融机构可在不同市场环境下灵活应对风险，推动金融稳定。
原文摘要 · Abstract (English)
Large language models (LLMs) have emerged as powerful tools in the field of finance, particularly for risk management across different asset classes. In this work, we introduce a Cross-Asset Risk Management framework that utilizes LLMs to facilitate real-time monitoring of equity, fixed income, and currency markets. This innovative approach enables dynamic risk assessment by aggregating diverse data sources, ultimately enhancing decision-making processes. Our model effectively synthesizes and analyzes market signals to identify potential risks and opportunities while providing a holistic view of asset classes. By employing advanced analytics, we leverage LLMs to interpret financial texts, news articles, and market reports, ensuring that risks are contextualized within broader market narratives. Extensive backtesting and real-time simulations validate the framework, showing increased accuracy in predicting market shifts compared to conventional methods. The focus on real-time data integration enhances responsiveness, allowing financial institutions to manage risks adeptly under varying market conditions and promoting financial stability through the advanced application of LLMs in risk analysis.
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