🤖 AI Summary
This study addresses the difficulty of large language model (LLM)-based financial agents in modeling the continuity and transition reliability of historical events by proposing a point-in-time constrained stock scoring framework that decouples multi-view base alphas from reliability-calibrated residual correction terms. Methodologically, the framework introduces a hierarchical memory layer and typed event agents to aggregate cross-firm event successor relationships via local pairing. It further integrates LLMs, graph neural networks, and reliability calibration techniques for event state modeling and residual correction. Experimental results demonstrate that the proposed framework doubles the information coefficient information ratio (ICIR), achieving net Sharpe ratios of 1.656 and 1.725 on U.S. and Hong Kong stock markets, respectively, significantly outperforming baseline models.
📝 Abstract
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.