用反证法验证能源股情绪与收益的关系，避免虚假相关。
Beyond Correlation: Refutation-Validated Aspect-Based Sentiment Analysis for Explainable Energy Market Returns
- 通过反证测试筛选出真实有效的情绪信号
- 仅少数情绪关联在多重检验中存活，且可再生能源响应具时效性
- 适合金融量化研究者参考其严谨的验证方法
本文提出一种基于反证验证的金融情绪分析框架，解决传统相关性研究无法区分真实关联与虚假关联的问题。以能源板块X数据为基础，测试个股层面情绪信号与股价回报之间的稳健关系。方法包括净比率评分、z标准化、含Newey-West HAC误差的OLS回归，以及安慰剂、随机共因、子集稳定性与自助法等反证检验。在六个能源股票中，仅有少数情绪关联通过全部检验；可再生能源的情绪响应具有显著的要素与时间窗口特异性。尽管未确立因果关系，该框架仍提供统计稳健、方向可解释的信号。受限于样本量（六只股票，一个季度），本研究定位为方法论概念验证。
原文摘要 · Abstract (English)
This paper proposes a refutation-validated framework for aspect-based sentiment analysis in financial markets, addressing the limitations of correlational studies that cannot distinguish genuine associations from spurious ones. Using X data for the energy sector, we test whether aspect-level sentiment signals show robust, refutation-validated relationships with equity returns. Our pipeline combines net-ratio scoring with z-normalization, OLS with Newey West HAC errors, and refutation tests including placebo, random common cause, subset stability, and bootstrap. Across six energy tickers, only a few associations survive all checks, while renewables show aspect and horizon specific responses. While not establishing causality, the framework provides statistically robust, directionally interpretable signals, with limited sample size (six stocks, one quarter) constraining generalizability and framing this work as a methodological proof of concept.
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