Accurate stock forecasts are most useful when paired with reliable uncertainty estimates for risk-aware decisions. We propose BiMamba & Bayesian MAGAC, a unified architecture that couples a bidirectional selective state-space temporal encoder with a Bayesian graph module for cross-asset dependencies. Our contributions are threefold. (i) Temporal modeling: a bidirectional selective SSM (BiMamba) that captures forward/backward dependencies with linear complexity \(\mathcal {O}(L)\) while preserving causality over the most recent L steps. (ii) Graph reasoning: a Bayesian Multi-head Adaptive Graph Attention Convolution (MAGAC) with: adaptive adjacency blending between a Gaussian locality prior and data-driven attention; node-conditioned, factorized spectral filtering that separates who interacts from how far information propagates; and DropEdge regularization. (iii) Uncertainty quantification: closed-form variance propagation under a diagonal approximation for \(\mathcal {O}(N)\) cost, complemented by lightweight Monte Carlo via MC-Dropout for calibrated posteriors. Compared with methods using fixed adjacencies or expensive full-covariance estimates, our design yields efficient, uncertainty-aware predictions with a modest \(\sim 2\times \) overhead. Training employs a heteroscedastic Gaussian negative log-likelihood to jointly optimize accuracy and calibration end-to-end. Across benchmarks, the model delivers consistent predictive gains and better-calibrated risk estimates, supporting practical uses such as position sizing and downside protection. Implementation: https://github.com/ngngsonan/MAMBA_BGNN .