Diffusion-Augmented Reinforcement Learning for Robust Portfolio Optimization under Stress Scenarios
This work addresses portfolio management challenges for investors and asset managers by enhancing robustness in financial applications, though it is incremental as it builds on existing DRL and DDPM methods.
The paper tackles portfolio optimization under stress scenarios by proposing Diffusion-Augmented Reinforcement Learning (DARL), which integrates Denoising Diffusion Probabilistic Models with Deep Reinforcement Learning to generate synthetic market crash data, resulting in superior risk-adjusted returns and resilience against crises like the 2025 Tariff Crisis.
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.