Abstract
This study presents a novel transformer-based model specifically designed for financial forecasting, integrating
explainability mechanisms such as SHAP (SHapley Additive exPlanations) values and attention visualizations to
enhance interpretability. Unlike previous models, which often compromise between accuracy and transparency, our
approach balances predictive accuracy with interpretability, allowing stakeholders to gain deeper insights into the
factors driving market changes. By revealing critical market influences through feature importance and attention maps,
this model provides both robustness and transparency, catering to the needs of high-stakes financial environments.