A New Method Based on Hierarchical Belief Rule Base with Balanced Accuracy and Interpretability for Stock Price Trend Prediction

The prediction of stock price trends is of vital importance for maintaining the stability of the financial market, optimizing resource allocation and preventing systemic risks. To ensure the practical application value of the prediction model, it is necessary to maintain prediction accuracy while ensuring that the output results of the model are interpretable, enabling decision-makers to understand and verify the prediction basis. Belief Rule Base (BRB) models, grounded in IF-THEN rule semantics, offer inherent interpretability. However, optimizing BRB models can erode this interpretability, and they are susceptible to combinatorial explosion in multi-attribute scenarios, disrupting the structural symmetry and escalating model complexity. To address these challenges while preserving both accuracy and interpretability symmetry, this paper proposes a new method based on hierarchical Belief Rule Base with balanced accuracy and interpretability (HBRB-b) for stock price trend prediction. First, a hierarchical model structure is constructed to overcome the rule combinatorial explosion problem, ensuring initial structural symmetry and interpretability. Second, several interpretability criteria specifically designed for stock prediction and compatible with maintaining model balance during optimization are proposed to guide the modeling process. Finally, an improved Whale Optimization Algorithm is proposed, incorporating constraints to preserve the interpretability symmetry throughout the optimization process. A case study validates the model’s effectiveness in stock price trend prediction. Comparative results demonstrate that the HBRB-b-based model achieves a favorable symmetry between prediction accuracy and model interpretability, offering distinct advantages in both aspects.