发表机构
Hanyang University（汉阳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出结合时间SHAP证据与历史类比的LLM叙述框架，通过外化可验证推理提升股票预测叙述的忠实度，实验显示证据忠实度显著提高。
AI 中文摘要
在金融领域，解释机器学习预测至关重要，然而可解释人工智能的数值输出对于非专业人士而言可能难以理解。虽然大语言模型（LLM）能够将这些输出转化为自然语言，但在推断数值变化和特征关系时，它们可能会产生错误。我们提出了一种用于横截面股票收益预测的LLM叙述框架，该框架将时间序列Shapley加性解释（SHAP）证据与历史制度类比相结合。时间序列证据追踪了XGBoost模型在六个月期间标准化全局SHAP重要性的变化。历史类比是过去具有类似SHAP重要性变化的时期，其模型表现和后续市场收益作为比较背景提供。利用该框架，我们进行了一项关于渐进式推理外化的受控研究，依次提供原始SHAP序列、确定性时间描述符和特征关系。每个生成的声明都通过与来源关联的证据进行验证。在Qwen3上，外化数值和关系推理提高了证据忠实度以及时间和关系准确性。对于Qwen3-32B-Instruct，证据忠实度从0.696增加到0.996。虽然历史类比并未改善结构化的自动忠实度，但它们获得了更高的人工评分的实用性分数。这些结果表明，外化可验证的推理增强了叙述的忠实度，并且历史背景增加了解释性价值。
英文摘要
In finance, interpreting machine learning predictions is essential, yet the numerical outputs of explainable AI can be difficult for non-experts to understand. While large language models (LLMs) can translate these outputs into natural language, they may produce errors when inferring numerical changes and feature relations. We propose an LLM narrative framework for cross-sectional stock return prediction that combines temporal Shapley additive explanations (SHAP) evidence with historical regime analogs. Temporal evidence tracks changes in the normalized global SHAP importance of an XGBoost model over six months. Historical analogs are past periods with similar changes in SHAP importance, their model performance and subsequent market returns are provided as comparative context. Using this framework, we conduct a controlled study of progressive reasoning externalization, sequentially providing raw SHAP sequences, deterministic temporal descriptors, and feature relations. Each generated claim is verified against provenance-linked evidence. Across Qwen3, externalizing numerical and relational reasoning improved evidence faithfulness as well as temporal and relational accuracy. Evidence faithfulness increased from 0.696 to 0.996 for Qwen3-32B-Instruct. While historical analogs did not improve structured automatic faithfulness, they received higher human-rated usefulness scores. These results suggest that externalizing verifiable reasoning enhances narrative faithfulness and that historical context adds interpretive value.
