通过LLM增强的语义网络实现跨股票可预测性
Cross-Stock Predictability via LLM-Augmented Semantic Networks
AI总结：
本文提出一种两阶段框架，利用LLM过滤候选边以提升文本衍生金融网络的经济准确性，从而增强跨股票预测能力。
摘要：
基于文本的金融网络日益被用于研究跨股票收益率可预测性。一种常见方法通过公司披露嵌入的相似性构建链接，但此类网络常包含虚假边，因为文本接近性不必然意味着经济联系。我们提出一种两阶段框架，首先从10-K嵌入构建稀疏候选图，然后利用大语言模型根据经济关系分类和过滤候选边。经过优化的图用于将对级别均值回归信号聚合为股票级别交易信号，使用关系感知和距离加权。在2011至2019年S&P 500成分股回测中，基于LLM的边过滤将长短期夏普比率从0.742提升至0.820，并将最大回撤从-10.47%降至-7.85%。这些结果表明，基于LLM的推理可以提升文本衍生金融网络的经济准确性并加强跨股票可预测性。
英文摘要：
Text-based financial networks are increasingly used to study cross-stock return predictability. A common approach constructs links from similarities in firms' disclosure embeddings, but such networks often contain spurious edges because textual proximity does not necessarily imply economic connection. We propose a two-stage framework that first builds a sparse candidate graph from 10-K embeddings and then uses a large language model to classify and filter candidate edges according to their economic relations. The refined graph is used to aggregate pair-level mean-reversion signals into stock-level trading signals with relation-aware and distance-based weights. In a backtest on S&P 500 constituents from 2011 to 2019, LLM-based edge filtering improves the long-short Sharpe ratio from 0.742 to 0.820 and reduces maximum drawdown from $-$10.47% to $-$7.85%. These results suggest that LLM-based reasoning can improve the economic fidelity of text-derived financial networks and strengthen cross-stock predictability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
