In this paper, we implement and evaluate a conditional diffusion model for asset return prediction and portfolio construction on large-scale equity data. Our method models the full distribution of future returns conditioned on firm characteristics (i.e.\ factors), using the resulting conditional moments to construct portfolios. We observe a clear bias--variance tradeoff: models conditioned on too few factors underfit and produce overly diversified portfolios, while models conditioned on too many factors overfit, resulting in unstable and highly concentrated allocations with poor out-of-sample performance. Through an ablation over factor dimensionality, we reveal an intermediate number of factors that achieves the best generalization and outperforms baseline portfolio strategies.

翻译：本文在大规模股票数据上实现并评估了一种用于资产收益预测与投资组合构建的条件扩散模型。该方法以公司特征（即因子）为条件对未来收益的完整分布进行建模，并利用所得条件矩构建投资组合。我们观察到明显的偏差-方差权衡：基于过少因子的模型会出现欠拟合，产生过度分散的投资组合；而基于过多因子的模型则会产生过拟合，导致配置不稳定且高度集中，样本外表现不佳。通过对因子维度的消融实验，我们发现存在一个中间因子数量能够实现最佳泛化性能，并超越基准投资组合策略。