We propose a novel conditional diffusion model for contextual portfolio optimization that learns the cross-sectional distribution of next-day stock returns conditioned on high-dimensional asset-specific factors. Our model leverages a Diffusion Transformer architecture with token-wise conditioning, which enables linking each asset's return to its own factor vector while capturing complex cross-asset dependencies. By drawing generative samples from the learned conditional return distribution, we perform daily mean-variance and mean-CVaR optimization, incorporating transaction costs and realistic constraints. Using data from the Chinese A-share market, we demonstrate that our approach consistently outperforms various standard benchmarks across multiple risk-adjusted performance metrics. Furthermore, we establish a 2-Wasserstein error bound for the conditional diffusion model and quantify how its distributional approximation errors propagate to the downstream portfolio optimization task. Our results demonstrate the potential of generative diffusion models for high-dimensional, risk-sensitive contextual stochastic optimization and financial decision making.

翻译：我们提出了一种新颖的条件扩散模型，用于情境化投资组合优化，该模型学习以高维资产特定因子为条件的次日股票收益的横截面分布。我们的模型采用了扩散Transformer架构及token级条件化方法，使得每项资产的收益能够与其自身的因子向量相关联，同时捕捉复杂的跨资产依赖关系。通过从学习到的条件收益分布中生成生成式样本，我们进行每日均值-方差和均值-CVaR优化，并纳入交易成本及实际约束条件。利用中国A股市场的数据，我们证明了该方法在多个风险调整绩效指标上始终优于各种标准基准。此外，我们为条件扩散模型建立了2-Wasserstein误差界，并量化了其分布近似误差如何向下游投资组合优化任务传播。我们的结果展示了生成式扩散模型在高维、风险敏感的情境化随机优化及金融决策中的潜力。