## 5 Discussion and Conclusions
By relying solely on historical prices and weighted moving averages (WMA), the model excludes potentially informative features such as trading volume, momentum-based indicators (e.g., KSI, MACD), and macroeconomic data. While this decision was made intentionally to control model complexity and focus on the stacking architecture, future studies should explore the effect of incorporating these variables on model accuracy and robustness. While the proposed stacking model demonstrated strong predictive performance, its interpretability remains limited. As with many ensemble learning approaches, the internal decision logic of the model functions as a black box. This lack of transparency may hinder adoption in practical financial settings in which understanding the rationale behind predictions is essential. In future work, we recommend the use of SHAP (SHapley Additive exPlanations) values or permutation importance to identify the most influential input features and provide more interpretable insights into the model's decision-making process. Key factors affecting stock prices consist of news regarding company activities, mergers, and investments, as well as the macroeconomic variables of the nation where the company functions. In upcoming research, we suggest adding these variables to the model after assessing their correlations with stock prices and the trustworthiness of the information sources. Incorporating these variables might enhance prediction accuracy and account for the influence of external factors on stock prices. In summary, although the proposed hybrid model has shown promising results, its evaluation remains limited to a narrow dataset and simplified input features. More extensive testing and enhancement are required before the model can be broadly applied. There remains considerable work to be accomplished in this field, particularly in improving generalization, interpretability, and real-time applicability across diverse market conditions. All authors ([PERSON], [PERSON] and [PERSON]) contributed to different aspects of this work. [PERSON] led the development of the hybrid model, coordinated the software implementation, and drafted the initial version of the manuscript. [PERSON] supported the implementation, conducted the data preprocessing and validation, and contributed to the manuscript revision. [PERSON] supervised the research process, contributed to the methodological design and data analysis, and provided guidance throughout this study. All authors have read and agreed to the published version of the manuscript. This work was supported by the Universidad Nacional Mayor de San Marcos--RR N\({}^{\circ}\) 004305-2024 and project code C24200721. Not applicable. The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author. We would like to express our sincere gratitude to the AI LAB at the Universidad Nacional Mayor de San Marcos, particularly to the Faculty of Systems Engineering, for their invaluable support and the resources provided to carry out this research. We also extend our appreciation to our colleagues for their insightful discussions, and to the reviewers of this paper, whose constructive feedback helped improve the quality and clarity of our study. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: