用知识图谱和规则链预测股票走势，解释清晰可审计。
TRACE: Temporal Rule-Anchored Chain-of-Evidence on Knowledge Graphs for Interpretable Stock Movement Prediction
- 基于经济规则引导多跳图遍历，聚焦有意义的推理路径。
- 在标普500上达55.1%准确率，召回率71.5%，显著优于基线。
- 结果可读可审，适合金融风控与决策支持场景使用。
我们提出一种时间规则锚定的证据链（TRACE）方法，用于知识图谱上的可解释股票走势预测。该方法将符号关系先验、动态图探索与大模型决策指导统一于端到端流程中。通过规则限制的多跳路径搜索，在同期新闻语境下构建候选推理链，并将完全接地的证据聚合为可审计的 exttt{UP}/ exttt{DOWN}结论，路径可读性强。在标普500基准上，该方法达到55.1%准确率、55.7%精确率、71.5%召回率和60.8%F1，超越强基线，且在相同评估条件下提升召回与F1。性能提升源于：(i) 规则引导的探索聚焦经济上合理的模式而非随机行走；(ii) 文本锚定的整合仅选择高置信度、完全接地的假设，而非均匀聚合弱信号。两者结合实现更高敏感度而不牺牲选择性，提供可信且可审计的解释。
原文摘要 · Abstract (English)
We present a Temporal Rule-Anchored Chain-of-Evidence (TRACE) on knowledge graphs for interpretable stock movement prediction that unifies symbolic relational priors, dynamic graph exploration, and LLM-guided decision making in a single end-to-end pipeline. The approach performs rule-guided multi-hop exploration restricted to admissible relation sequences, grounds candidate reasoning chains in contemporaneous news, and aggregates fully grounded evidence into auditable \texttt{UP}/\texttt{DOWN} verdicts with human-readable paths connecting text and structure. On an S\&P~500 benchmark, the method achieves 55.1\% accuracy, 55.7\% precision, 71.5\% recall, and 60.8\% F1, surpassing strong baselines and improving recall and F1 over the best graph baseline under identical evaluation. The gains stem from (i) rule-guided exploration that focuses search on economically meaningful motifs rather than arbitrary walks, and (ii) text-grounded consolidation that selectively aggregates high-confidence, fully grounded hypotheses instead of uniformly pooling weak signals. Together, these choices yield higher sensitivity without sacrificing selectivity, delivering predictive lift with faithful, auditably interpretable explanations.
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