Factor Dimensionality and the Bias–Variance Tradeoff in Diffusion Portfolio Models

Avi Bagchi, Michael Tesfaye, Om Shastri

Presentation Attendance: Yes, we will present in-person

Keywords: Diffusion Models, Time Series Modeling

TL;DR: Factor-conditioned diffusion models for portfolio construction exhibit a bias–variance tradeoff in factor dimensionality.

Abstract: 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.

Track: Industry and Application Track (max 2 pages)

Submission Number: 113