## 6 Conclusions  
In this article, we have introduced a hierarchical BERT-GRU model featuring a novel Query-Guided Dual Attention (QGDA) mechanism tailored for the sentiment analysis of stock-related documents. The QGDA mechanism is guided by domain-specific financial queries defined by experts and integrated into the GRU sentence encoder via two attention stages, significantly enhancing the model's performance and providing a new perspective of concept-level explanatory basis that can foster transparency in the financial domain.  
We conducted experiments on a real-world dataset with various baseline and ablation models to illustrate the efficacy of our proposed approach incorporating the QGDA mechanism. Additionally, we designed a case study involving stock price fluctuations to examine the concept-level explanatory basis (derived from QGDA's query attention) and assess its robustness and applicability in securities analysis. The results demonstrate the superiority of our approach over baseline models, particularly highlighting the QGDA mechanism's contribution to providing a validated explanatory basis. Crucially, by identifying which conceptual drivers are more indicative of market movements, our model offers financial professionals a transparent 'why' behind sentiment predictions. This can empower more informed investment decision-making and processes, helping to both mitigate risks and optimize strategies.  
However, it is important to acknowledge the limitations associated with our current study. While extensive, the scope of our experiments was confined to a specific dataset, which may impact the generalizability of our findings across broader financial documents. Moreover, the practical application of our model in real-world financial analysis might encounter challenges, such as varying data quality and the dynamic nature of financial markets that could affect the model's predictive accuracy and explanatory power.  
Looking ahead, we plan to extend our research to include larger and more diverse datasets, aiming to further evaluate the model's generalizability across various stock-related documents. Additional experiments are also planned in order to more thoroughly verify the reliability of our financial concept-based query set and the resulting explanatory basis. In addition to financial analysis, the adaptability of our QGDA framework holds promise for explainable AI applications in other specialized fields. In particular, we envision integrating QGDA into retrieval-augmented generation (RAG) frameworks to provide structured and explainable evidence for LLMs, thereby enhancing their reliability in domain-specific tasks. This effort lays the groundwork for future research into synergistic systems that combine the strengths of specialized models and large language models to create transparent and valuable tools for experts across diverse sectors.  
[PERSON] contributed to conceptualization, methodology, software, investigation, and writing--original draft. [PERSON] contributed to visualization, data curation, and writing--review. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the talent introduction project of the School of Finance and Economics at Anhui Science and Technology University, titled \"Research on Data-Driven Chance Constraint Optimization Problem with Application to Portfolio Management\" (grant number CJYJ202401). The APC was funded by the authors.  
Not applicable.  
The datasets generated during the current study are not publicly available but are available from the corresponding author on reasonable request.  
The authors declare that there are no conflicts of interest regarding the publication of this paper.