标题： 建模企业层面的ESG情感互动在股票收益中的作用：使用改装词嵌入对16家公司的实证研究 显示英文标题

标题： Modeling Firm-Level ESG-Sentiment Interactions in Stock Returns: Evidence from 16 Companies Using Retrofitted Word Embeddings

摘要： 本研究探讨了金融新闻标题中嵌入的情绪特定情感如何与企业层面的环境、社会和治理(ESG)评级相互作用，从而影响股票回报行为。 解决现有文献中的关键方法论差距，分析利用了改造后的词嵌入来编码针对ESG相关叙述的离散情感线索。 与之前依赖于基于词典或基于变压器的模型的研究不同，这种方法明确结合了领域特定的情感语义，同时考虑了企业层面的异质性和时间性情感波动。 使用16家跨国公司的数据集和从Seeking Alpha标题中提取的情感数据，本研究测试了三个假设：(1)情绪特定情感独立预测股票回报；(2)情感的调节效应在ESG维度中有所不同；以及(3)积极（消极）情感增强（抑制）ESG绩效效应。 分析实施了一种双重情感聚合策略，并引入了一个三重显著性过滤标准以识别稳健的交互作用。 结果支持假设1和假设2，情绪如期待和信任在不同企业和ESG类别中与回报变化表现出一致的关联。 然而，对于假设3的结果是混合的：虽然一些情感-ESG组合符合理论预期，但许多矛盾的交互作用表现出更强的效果。 在本研究中，改造后的嵌入在解释ESG情感交互模型中的股票回报变化方面优于NRC情感词典，强调了情感细微差别在ESG-金融建模中的价值。 这些结果强调了情绪敏感的情感建模在理解投资者行为和与ESG相关的股票价格波动中的重要性。 显示英文摘要

摘要： This study investigates how emotion-specific sentiment embedded in financial news headlines interacts with firm-level Environmental, Social, and Governance (ESG) ratings to influence stock return behavior. Addressing key methodological gaps in existing literature, the analysis leverages Retrofitted Word Embeddings to encode discrete emotional cues tailored to ESG-relevant narratives. Unlike prior studies that rely on lexicon-based or transformer-based models, this approach explicitly incorporates domain-specific emotional semantics while accounting for firm-level heterogeneity and temporal sentiment fluctuations. Using a dataset of 16 multinational firms and sentiment data extracted from Seeking Alpha headlines, the study tests three hypotheses: (1) emotion-specific sentiment independently predicts stock returns; (2) the moderating effect of sentiment varies across ESG dimensions; and (3) positive (negative) sentiment amplifies (dampens) ESG performance effects. The analysis implements a dual sentiment aggregation strategy and introduces a triple-significance filtering criterion to identify robust interactions. Results support Hypotheses 1 and 2, with emotions such as anticipation and trust showing consistent associations with return variation across firms and ESG categories. However, findings for Hypothesis 3 are mixed: while some sentiment-ESG combinations align with theoretical expectations, many contradictory interactions exhibit stronger effects. In this study, retrofitted embeddings outperform the NRC Emotion Lexicon in explaining stock return variation within ESG-sentiment interaction models, underscoring the value of emotional nuance in ESG-finance modeling. These results underscore the importance of emotion-sensitive sentiment modeling in understanding investor behavior and ESG-related stock price movements.