用于跨实体信号传播和阿尔法发现的大语言模型增强动态金融知识图谱
LLM-Enhanced Dynamic Financial Knowledge Graphs for Cross-Entity Signal Propagation and alpha discovery
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中文总结 AI 辅助
研究针对传统金融NLP忽略跨实体信息扩散问题，开发基于大语言模型的金融测量和信号传播框架，构建动态金融知识图谱，通过社区感知机制传播信号，引入新金融信号及测试，实验表明该框架能准确恢复网络结构等，有增量预测能力。
中文摘要 AI 辅助
金融信息很少孤立地影响单个公司。盈利惊喜、资本支出变化、供应限制和业绩指引修订等会通过供应商、客户、竞争对手和技术生态系统网络传播。传统金融自然语言处理主要衡量直接提及公司的文档层面情绪，常忽略跨实体信息扩散。本文开发了基于大语言模型的金融测量和信号传播框架。大语言模型将非结构化金融文档转换为结构化经济状态变化事件，提取明确和隐含的公司关系以构建动态金融知识图谱。然后使用社区感知机制通过估计网络传播事件信号，使信息在动态检测的经济社区内比跨社区边界传播更强。引入基于网络的金融信号社区信息惊喜（CIS）和传播信息惊喜（PIS）并开发相应计量测试。时变经济社区的受控模拟表明，该框架能准确恢复潜在网络结构，检测新投资生态系统的出现，并生成具有超越情绪和直接大语言模型事件信号的增量预测能力的传播信号。在重复模拟中，社区感知传播在五个嵌套基准中实现最强的排名信息系数和多空夏普比率。第二次罗素1000校准模拟证实，在更稀疏网络、异质新闻报道、现实大盘波动和较小效应规模下主要结果依然成立。
英文摘要
Financial information rarely affects a single company in isolation. Earnings surprises, capital expenditure changes, supply constraints, and guidance revisions can propagate through networks of suppliers, customers, competitors, and technology ecosystems. Traditional financial NLP primarily measures document-level sentiment for the directly mentioned company and often ignores cross-entity information diffusion. This paper develops an LLM-based financial measurement and signal propagation framework. The LLM converts unstructured financial documents into structured economic state-change events and extracts explicit and implicit corporate relationships to construct a dynamic financial knowledge graph. Event signals are then propagated through the estimated network using a community-aware mechanism, allowing information to diffuse more strongly within dynamically detected economic communities than across community boundaries. We introduce Community Information Surprise, CIS, and Propagated Information Surprise, PIS, as network-based financial signals and develop corresponding econometric tests. Controlled simulations with time-varying economic communities show that the framework accurately recovers latent network structure, detects the emergence of new investment ecosystems, and generates propagated signals with incremental predictive power beyond sentiment and direct LLM event signals. Across repeated simulations, community-aware propagation achieves the strongest rank information coefficient and long-short Sharpe ratio among five nested benchmarks.A second Russell 1000 calibrated simulation confirms that the main results persist under sparser networks, heterogeneous news coverage, realistic large-cap volatility, and smaller effect sizes.
