Diffusion-based Uncertainty-Utility Alignment for Portfolio Optimization

Portfolio optimization requires investment decisions that balance return and risk under uncertainty. Probabilistic forecasting provides an explicit representation of future return uncertainty. However, predictive uncertainty is not inherently aligned with investment utility: portfolio decisions can be sensitive to scenario-sampling variability, while forecasting-only training does not directly account for the investment consequences of the resulting weights. In this paper, we propose diffusion-based uncertainty–utility alignment (DualPO) to align both the exploitation and generation of predictive uncertainty with investment utility. Specifically, DualPO trains an uncertainty-conditioned allocator using differentiable distributional summaries of return scenarios generated by a pretrained diffusion forecaster, aligning portfolio decisions with investment utility. Meanwhile, DualPO backpropagates utility gradients through the allocator to adapt the forecaster, aligning scenario generation with investment utility while retaining denoising supervision. Experimental results on the S&P and CSI benchmarks show that DualPO outperforms state-of-the-art baselines.