RICE-Alpha：基于事件图与可靠性校正的LLM智能体股票预测
RICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting
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中文总结 AI 辅助
提出RICE-Alpha框架，通过事件图建模历史事件延续，以可靠性校准的残差校正增强LLM智能体股票预测，在纳斯达克100和恒生指数上取得最优表现。
中文摘要 AI 辅助
与股票相关的新闻通过时间上相互依赖的公司事件演变，使得历史信息只有在事件连续性、信息可用性和转换可靠性被建模时才有用。现有的基于LLM的金融智能体整合了历史证据，但在时点约束下保留发行人特定时间顺序以及识别历史转换何时为当前预测提供额外信息方面，支持有限。我们提出了RICE-Alpha（基于事件图的可靠性校正），一个时点股票评分框架，将具有历史感知的多视图基础Alpha与从历史事件延续中推导的可靠性校准残差校正分开。一个多层记忆层将新闻解读基于时间上符合条件的发行人特定历史，而一个类型化事件智能体构建事件状态，其后续关系在发行人内部形成，并且仅在有效的局部配对后跨公司汇集。成熟转换通过其经验可靠性进行校准，所得图信号对基础Alpha和技术视图进行残差化，以获得RICE Delta。在2024年至2026年的每日纳斯达克100和恒生指数面板上，RICE-Alpha在评估的LLM智能体和动量中，在四个预测和四个组合层面指标上取得了最强结果。其ICIR比最强基线高出两倍以上，而净夏普比率在美国和香港分别达到1.656和1.725。美国消融研究进一步显示，在移除主要组件后，经Holm调整的IC和RankIC显著下降。这些结果表明，当历史事件延续在时间上扎根、可靠性校准并作为多视图预测的残差校正引入时，能增加增量信息。
英文摘要
Equity-relevant news evolves through temporally dependent corporate events, making historical information useful only when event continuity, information availability, and transition reliability are modeled. Existing LLM-based financial agents incorporate historical evidence, yet they provide limited support for preserving issuer-specific chronology under point-in-time constraints and for identifying when historical transitions contribute information beyond the current forecast. We present RICE-Alpha (Reliability-Informed Correction with Event Graphs), a point-in-time stock-scoring framework that separates a history-aware multi-view Base Alpha from a reliability-calibrated residual correction derived from historical event continuation. A Multi-Tier Memory Layer grounds news interpretation in temporally eligible issuer-specific history, while a Typed Event Agent constructs event states whose successor relations are formed within issuers and pooled across firms only after valid local pairing. Matured transitions are calibrated by their empirical reliability, and the resulting graph signal is residualized against the Base Alpha and technical view to obtain the RICE Delta. On daily Nasdaq-100 and Hang Seng Index panels from 2024 to 2026, RICE-Alpha achieves the strongest results among the evaluated LLM-based agents and momentum across four predictive and four portfolio-level metrics. Its ICIR more than doubles that of the strongest baseline, while net Sharpe ratios reach 1.656 and 1.725 in the U.S. and Hong Kong, respectively. U.S. ablations further show significant reductions in IC and RankIC after Holm adjustment when major components are removed. These results indicate that historical event continuation adds incremental information when it is temporally grounded, reliability-calibrated, and introduced as a residual correction to a multi-view forecast.
发表机构
- Zircon Security
- Utrecht University（乌得勒支大学）
- The Max Planck Institute for Psycholinguistics（马克斯·普朗克心理语言学研究所）
- China Life R&D Center（中国人寿研发中心）
机构由 AI 辅助整理，请以论文原文为准。
