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Factor-Based Conditional Diffusion Model for Portfolio Optimization

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  • Xuefeng Gao
  • Mengying He
  • Xuedong He

Abstract

We propose a novel conditional diffusion model for portfolio optimization that learns the cross-sectional distribution of next-day stock returns conditioned on asset-specific factors. The model builds on the Diffusion Transformer with token-wise conditioning, linking each asset's return to its own factor vector while capturing cross-asset dependencies. Generated return samples are used for daily mean-variance optimization under realistic constraints. Empirical results on the Chinese A-share market show that our approach consistently outperforms benchmark methods based on standard empirical and shrinkage-based estimators across multiple metrics.

Suggested Citation

  • Xuefeng Gao & Mengying He & Xuedong He, 2025. "Factor-Based Conditional Diffusion Model for Portfolio Optimization," Papers 2509.22088, arXiv.org.
    • Handle: RePEc:arx:papers:2509.22088
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    References listed on IDEAS

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    1. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    2. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    3. Victor DeMiguel & Lorenzo Garlappi & Francisco J. Nogales & Raman Uppal, 2009. "A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms," Management Science, INFORMS, vol. 55(5), pages 798-812, May.
    4. Minshuo Chen & Renyuan Xu & Yumin Xu & Ruixun Zhang, 2025. "Diffusion Factor Models: Generating High-Dimensional Returns with Factor Structure," Papers 2504.06566, arXiv.org, revised Jul 2025.
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