Beyond Correlation: Refutation-Validated Aspect-Based Sentiment Analysis for Explainable Energy Market Returns

Abstract

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.

Figures (6)

• Figure 1: Directed acyclic graph (DAG) representing the assumed causal structure for sentiment--return analysis. The target estimand is $\beta_i$, the effect of lagged aspect sentiment $z_{a,t-l}$ on returns $r_{i,t}$. Observed controls $X_t$ (lagged returns, sentiment activity) are included in the OLS specification. Dashed elements represent unobserved confounders $U$ (e.g., private information flows, algorithmic trading patterns) whose influence refutation tests help assess sensitivity to. Refutation testing cannot eliminate confounding but provides bounded confidence that estimates are not purely artifactual.
• Figure 2: Dot-and-whisker plot of bootstrap 95% confidence intervals for top signals. Blue: associations passing all four refutation tests; grey: filtered associations failing $\geq 1$ test. Coefficient labels in basis points (×100). The dashed red line marks zero.
• Figure 3: Stem plot of market sentiment coefficients across lags 0–3 for NextEra. Only the lag-2 coefficient (blue) survives all four refutation tests, with temporal decay evident at lag 3.
• Figure 4: Heatmap of validated sentiment--return associations across aspects and lags. Colored cells show refutation-validated coefficients ($\hat{\beta} \times 100$, basis points per standard deviation); gray cells indicate associations failing at least one refutation test. The clustering of positive effects at lags 1--2 with a negative inflation effect at lag 3 suggests aspect-specific temporal dynamics in information incorporation.
• Figure 5: Comparison of correlational effect sizes (red, Pearson $|r|$) versus refutation-validated regression coefficients (green, $|\hat{\beta}|$ in daily return units). Raw correlations range from 0.45 to 0.73, while validated effects are an order of magnitude smaller (0.034--0.048), illustrating the substantial "deflation" that occurs when spurious associations are filtered through systematic robustness testing.