## 5 Experimental Results and Evaluation
### Effect of Explainable Basis
This case study was designed to test the utility of the concept-level explainable basis provided by our model. It emphasizes the correlation between the new perspectives offered by our model and the predictive capabilities of the corresponding articles. This method not only enhances our understanding of the model's explanatory ability but also demonstrates how analyzing the attention weights generated by different queries can precisely capture key factors influencing the predictive ability of review articles for stock market movements. For the purposes of this case study, we define the price fluctuation of the related stock as the change in price from the day of the article's release to the subsequent 30 non  
Figure 3: Comparison of attention weight distributions between different sentences in the same article. suspended trading days. Specifically, we simplified the analysis of stock price fluctuations into three categories: Rising (when the stock price increases by more than 10%), Falling (when the stock price decreases by more than 10%), and Other (when the stock price fluctuates within a 10% range). To determine whether the price fluctuation is in accordance with the article's prediction, we classify the sentiment category labels as \"buy\" and \"hold\" to indicate an expected rise, \"sell\" to indicate an expected fall, and \"follow\" to indicate other. Based on the experimental settings outlined above, we found that the stock review articles in our dataset have an accuracy of only 31% in predicting subsequent stock fluctuations. To conduct a quantitative analysis of our proposed novel explainable basis derived from the query attention weights in QGDA, we propose the following hypothesis: the query category receiving the highest attention weight from the QGDA mechanism during article classification represents the main financial concept or perspective used by the article to analyze the stock and draw its final conclusion. We then compare the accuracy of stock fluctuation prediction between different main perspectives (dominant query categories) identified by our QGDA model and the average prediction accuracy of our dataset. To bolster the credibility of this hypothesis, we undertook an evaluation of the variations in accuracy across diverse classifications. This approach aimed to enhance the robustness of our findings regarding the disparity in accuracy among different categorizations produced by our proposed explainable basis. To ensure the methodological rigor of our case study, we focused our analysis solely on the test dataset comprising 4240 instances from the total dataset. Furthermore, to substantiate the effectiveness of our explainability experiments and avoid potential influence stemming from stochastic variability, we conducted a series of 100 classification experiments on the dataset using completely random sampling. This comprehensive validation approach was undertaken in order to empirically substantiate the explainable basis of our model's QGDA mechanism, thereby reinforcing the significance of its role as a discerning factor in classification explainability. As shown in Figure 4, significant variation is observed in the accuracy of subsequent stock price predictions across articles grouped by different dominant query perspectives (identified by the highest weight query from QGDA). Notably, articles where \(q_{4}\) (Financial Analysis) is the dominant query category during QGDA classification exhibit the highest accuracy in predicting stock prices. This is likely because financial analysis content often provides more direct, company-specific, and actionable signals (e.g., earnings, valuation) that correlate closely with immediate stock movements. In contrast, those dominated by \(q_{2}\) (Industry Analysis) are close to average and \(q_{1}\) (Macroeconomic Analysis) significantly lower, as their influences are generally broader and less immediately tied to individual stock performance. The difference in accuracy between the highest- and lowest-performing classes reached a substantial 18.18%.