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

Paper: arXiv 2510.07099

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.

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]

Cited References (17)

1. Enhancing deep reinforcement learning for stock trading: a reward shaping approach via expert feedback (2025) — link
2. FinXplore: An Adaptive Deep Reinforcement Learning Framework for Balancing and Discovering Investment Opportunities (2025) — link
3. Risk-Adjusted Deep Reinforcement Learning for Portfolio Optimization: A Multi-reward Approach (2025) — link
4. Deep Reinforcement Learning for Investor-Specific Portfolio Optimization: A Volatility-Guided Asset Selection Approach — → 2505.03760
5. Dynamic Reinforced Ensemble using Bayesian Optimization for Stock Trading (2024) — link
6. New Paradigm for Economic and Financial Research With Generative AI: Impact and Perspective (2024) — link
7. FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning (2022) — link
8. Scenario generation for market risk models using generative neural networks (2021) — link

1 of 17 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.