Diffusion-Augmented Reinforcement Learning for Robust Portfolio Optimization under Stress Scenarios

ArXiv ID: 2510.07099 “View on arXiv”

Authors: Himanshu Choudhary, Arishi Orra, Manoj Thakur

Abstract

In the ever-changing and intricate landscape of financial markets, portfolio optimisation remains a formidable challenge for investors and asset managers. Conventional methods often struggle to capture the complex dynamics of market behaviour and align with diverse investor preferences. To address this, we propose an innovative framework, termed Diffusion-Augmented Reinforcement Learning (DARL), which synergistically integrates Denoising Diffusion Probabilistic Models (DDPMs) with Deep Reinforcement Learning (DRL) for portfolio management. By leveraging DDPMs to generate synthetic market crash scenarios conditioned on varying stress intensities, our approach significantly enhances the robustness of training data. Empirical evaluations demonstrate that DARL outperforms traditional baselines, delivering superior risk-adjusted returns and resilience against unforeseen crises, such as the 2025 Tariff Crisis. This work offers a robust and practical methodology to bolster stress resilience in DRL-driven financial applications.

Keywords: Diffusion Probabilistic Models, Reinforcement Learning, Stress Testing, Market Crash Simulation, Portfolio Management, Equities

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Holy Grail
  • Why: The paper involves advanced mathematics including Diffusion Probabilistic Models and deep reinforcement learning (PPO) with formal loss functions and conditioning variables, but provides empirical validation on real-world financial data (Dow 30 stocks) with transaction costs and clear out-of-sample testing, indicating a balance of theoretical and practical rigor.
  flowchart TD
    G["Research Goal:<br>Enhance Portfolio Robustness<br>under Stress Scenarios"] --> D["Inputs: Historical Market Data<br>Investor Preferences"]
    D --> M1["Method: Diffusion Model (DDPM)<br>Generate Synthetic Crash Scenarios"]
    D --> M2["Method: Deep Reinforcement Learning<br>Train Policy Agent"]
    M1 --> C["Integration: Condition DRL<br>on Stress Scenarios"]
    M2 --> C
    C --> F["Outcome: DARL Framework<br>Superior Risk-Adjusted Returns<br>Resilience to 2025 Tariff Crisis"]