Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information

ArXiv ID: 2501.17992 “View on arXiv”

Authors: Unknown

Abstract

We develop a portfolio allocation framework that leverages deep learning techniques to address challenges arising from high-dimensional, non-stationary, and low-signal-to-noise market information. Our approach includes a dynamic embedding method that reduces the non-stationary, high-dimensional state space into a lower-dimensional representation. We design a reinforcement learning (RL) framework that integrates generative autoencoders and online meta-learning to dynamically embed market information, enabling the RL agent to focus on the most impactful parts of the state space for portfolio allocation decisions. Empirical analysis based on the top 500 U.S. stocks demonstrates that our framework outperforms common portfolio benchmarks and the predict-then-optimize (PTO) approach using machine learning, particularly during periods of market stress. Traditional factor models do not fully explain this superior performance. The framework’s ability to time volatility reduces its market exposure during turbulent times. Ablation studies confirm the robustness of this performance across various reinforcement learning algorithms. Additionally, the embedding and meta-learning techniques effectively manage the complexities of high-dimensional, noisy, and non-stationary financial data, enhancing both portfolio performance and risk management.

Keywords: Reinforcement Learning (RL), Generative Autoencoders, Meta-Learning, High-Dimensional State Space, Portfolio Allocation, Equities

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning concepts including dynamic embedding, generative autoencoders, and online meta-learning, which requires significant mathematical sophistication. Empirically, it is backed by rigorous testing on 500 U.S. stocks over 30 years, with comprehensive ablation studies, transaction cost considerations, and benchmark comparisons.

  flowchart TD
    A["Research Goal:<br>Portfolio Allocation in High-Dim,<br>Non-Stationary Markets"] -->|Input| B["Top 500 U.S. Stocks<br>(High-Dim, Noisy Data)"]
    B --> C["Dynamic Embedding Module<br>Generative Autoencoders"]
    C --> D["Reduced State Representation<br>(Low-Dim, Stationary Features)"]
    D --> E["RL Framework<br>Online Meta-Learning"]
    E --> F["Portfolio Allocation<br>Agent Decision"]
    F --> G["Outcomes:<br>Outperforms Benchmarks & PTO<br>Robust to Market Stress<br>Effective Volatility Timing"]