LENS 框架用 LLM 先验与新闻语义图预测股票收益
LENS: Reliability-Aware Stock Prediction via LLM-Extracted Priors and News-Driven Semantic Graphs
这篇论文把 LLM 提取的公司关系图和新闻语义图结合起来预测股票，在 S&P 500 上比最强基线 AR@5 高了 16.4%，做法挺新颖的。
论文提出 LENS（LLM-Extracted priors and News-driven Semantics），一个静态-动态图框架用于股票收益预测与排名。静态侧从 LLM 提取多关系公司图，并用多次运行的置信度加权降低低一致性边的权重；动态侧基于新闻构建语义图，用指数衰减的时间聚合捕捉市场变化。门控融合模块按每只股票、每天的比例重新平衡两个图。在 S&P 500 上，LENS 较最强基线将 AR@5 提升 16.4%、SR@5 提升 22.6%；在 NASDAQ 100 上分别提升 15.5% 和 20.9%。
LENS: Reliability-Aware Stock Prediction via LLM-Extracted Priors and News-Driven Semantic Graphs
Predicting stock returns requires modeling persistent company relations and time-varying market signals. Graph Neural Network (GNN) approaches combine static priors with dynamic relational graphs, but treat every extracted edge as a trustworthy binary fact, ignoring heterogeneous reliability across relational sources. We propose LENS (LLM-Extracted priors and News-driven Semantics), a static--dynamic graph framework for stock return prediction and ranking that models relational reliability as a first-class signal across the pipeline. On the static side, we extract a multi-relational company graph from a Large Language Model and aggregate it over multiple runs with confidence weighting to downweight low-consistency edges. On the dynamic side, we build a news-driven semantic graph with exponential-decay temporal aggregation to capture changing market context under uneven coverage. A context-dependent gated fusion module rebalances the two graphs at the per-stock, per-day level. On S&P 500, LENS improves AR@5 by 16.4% and SR@5 by 22.6% over the strongest baseline, and on NASDAQ 100 it achieves comparable improvements (15.5% and 20.9% respectively). Ablations isolate each component's contribution, and a human-validation study of sampled relations supports that the aggregation is grounded, providing evidence that reliability-aware LLM relations and news-based semantic graphs carry complementary information for graph-based financial prediction.