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.

翻译：我们提出了一种基于知识图谱的时间规则锚定证据链（TRACE）方法，用于可解释的股票走势预测。该方法将符号化关系先验、动态图谱探索以及大语言模型引导的决策制定统一在一个端到端的流程中。该方法执行规则引导的多跳探索，探索过程被限制在允许的关系序列内，将候选推理链锚定于同期新闻，并将完全锚定的证据聚合成可审计的 \texttt{上涨}/\texttt{下跌} 判断，同时提供连接文本与结构的人类可读路径。在标准普尔500指数基准测试中，该方法实现了55.1%的准确率、55.7%的精确率、71.5%的召回率和60.8%的F1分数，超越了现有强基线，并在相同评估条件下较最佳图谱基线提升了召回率与F1分数。性能提升源于：（i）规则引导的探索将搜索聚焦于具有经济意义的模式而非随机游走；（ii）文本锚定的整合过程选择性地聚合高置信度、完全锚定的假设，而非均匀地汇集弱信号。这些设计选择共同实现了在不牺牲选择性的前提下获得更高的敏感性，从而在提供预测性能提升的同时，生成忠实可靠、可审计解释的说明。