大型语言模型驱动的时间序列预测金融网络指标

Mini Han Wang^1,2, Ying Yeung³

¹The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.

Frontiers in artificial intelligence

|February 16, 2026

概括

本研究介绍了一种大型语言模型 (LLM) 框架,用于预测金融市场网络,改善系统风险的预测,并为投资者提供经济洞察力.

科学领域:

金融网络分析金融网络分析
系统风险预测系统风险预测
在金融领域的机器学习.

背景情况:

金融市场是复杂的网络,机构持有影响信息流和风险传播.
预测库存网络演变对于稳定性至关重要,但由于数据的复杂性和非静止性而具有挑战性.

研究的目的:

为金融网络开发一个可解释的基于LLM的预测框架.
预测整个市场的度和企业级异常,以便早期发现风险.

主要方法:

从2016-2024年构建的时间指数双方基金股票图表.
计算网络指标:集中程度 (cen_d) 和残余密度 (den).
开发了一个LLM框架,集成时间序列,文本披露和检索增强的背景,用于多步预测.

主要成果:

在准确性方面,LLM框架显著超过了ARIMA,Prophet和时间融合变压器的基准.
注意力分析显示,对季度具有基金共同流动或政策冲击的季度来说,模型的重要性.
在预测网络指标方面,已证明减少了错误和改进了定向准确性.

结论:

基于LLM的预测为系统风险和可解释的见解提供了早期警告.
基于语言的图表预测为金融市场监督和政策提供了一个新的范式.

关键词:

的程度集中集中化.金融网络分析金融网络分析大型语言模型 (LLM)剩余密度剩余密度提取增强生成 (RAG) 的方法股票信息网络股票信息网络系统风险和市场稳定性时间序列预测时间序列预测

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