Factor Dimensionality and the Bias-Variance Tradeoff in Diffusion Portfolio Models
This addresses portfolio optimization for financial investors by identifying a bias-variance tradeoff in factor dimensionality, though it appears incremental as it focuses on parameter tuning within an existing model framework.
The paper tackles the problem of determining the optimal number of factors in diffusion models for asset return prediction and portfolio construction, finding that an intermediate number of factors achieves the best generalization and outperforms baseline strategies by balancing underfitting and overfitting.
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.