Beyond Correlation: Refutation-Validated Aspect-Based Sentiment Analysis for Explainable Energy Market Returns
This provides a methodological proof of concept for statistically robust sentiment signals in energy market returns, though it is incremental due to limited sample size and generalizability.
The paper tackles the problem of distinguishing genuine from spurious associations in aspect-based sentiment analysis for financial markets by proposing a refutation-validated framework, resulting in only a few robust associations surviving tests across six energy tickers with renewables showing specific responses.
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.