Keywords
Stock Prediction
Abstract
The rapid advancement of deep learning models in financial prediction has yielded remarkable accuracy improvements, yet the inherent opacity of these models poses significant challenges for regulatory compliance, trust-building, and practical deployment in high-stakes financial environments. This paper presents a comprehensive investigation into the intersection of explainable AI (XAI) and financial prediction, with a particular focus on graph-based neural networks and regularization techniques that can simultaneously enhance model accuracy and provide interpretable predictions. We propose a novel framework that adapts explanation-based bias decoupling regularization—originally developed for natural language processing tasks—to the domain of stock trend prediction. Our approach, termed Explainable Stock State Space Graph (E-S3G), extends the Stock State Space Graph architecture by integrating interpretability mechanisms that reveal the underlying factors driving prediction decisions. Through extensive experiments on benchmark financial datasets, we demonstrate that the proposed framework achieves competitive prediction accuracy while significantly improving model explainability. Furthermore, we provide a thorough review of 15 references encompassing graph neural networks for financial prediction, explainability methods, and regularization techniques, offering a holistic perspective on the current state and future directions of trustworthy AI in finance.
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