Uncertainty Quantification and Risk-Aware Stock Prediction: A Bayesian Graph-Based State Space Approach

Abstract

Stock market prediction inherently involves significant uncertainty arising from the stochastic nature of market dynamics, incomplete information, and the complex interdependencies among financial variables. While the Stock State Space Graph (S3G) model has demonstrated superior predictive accuracy by integrating state space modeling with graph-based relational reasoning, it provides only point predictions without quantifying the uncertainty associated with these predictions. This limitation is critical in financial applications where risk management and decision-making under uncertainty are paramount. This paper proposes a novel framework, Uncertainty-Aware Stock State Space Graph (UA-S3G), that extends the S3G architecture through Bayesian uncertainty quantification. Our approach distinguishes between epistemic uncertainty (model uncertainty arising from insufficient training data) and aleatoric uncertainty (inherent data noise) by employing variational inference and Monte Carlo dropout techniques. We adapt explanation-based bias decoupling regularization principles from Zang and Liu's work on natural language inference to improve uncertainty calibration in the presence of spurious correlations. Through extensive experiments on benchmark financial datasets, we demonstrate that UA-S3G produces well-calibrated prediction intervals and substantially outperforms baseline methods in risk-aware trading scenarios. Our work contributes to the growing body of research on trustworthy AI in finance, providing principled uncertainty estimates that enable more robust and reliable stock prediction systems.