用动态嵌入和强化学习，让投资组合在动荡市场中更聪明地调仓。
Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information
- 用生成自编码器+在线元学习，动态压缩复杂市场数据
- 在美股前500只股票上表现优于传统模型，尤其在危机时期
- 适合想提升量化投资风控能力的研究者和从业者
我们提出一种投资组合配置框架，利用深度学习应对高维、非平稳且信噪比低的市场信息挑战。方法包含动态嵌入技术，将复杂的非平稳高维状态空间降维为低维表示。设计的强化学习（RL）框架结合生成自编码器与在线元学习，动态嵌入市场信息，使RL智能体聚焦于对投资决策最具影响的状态部分。基于美国前500只股票的实证分析表明，该框架优于常见基准及机器学习中的预测-优化（PTO）方法，尤其在市场压力时期表现突出。传统因子模型无法完全解释其优异性能。该框架通过择时波动率，降低动荡时期的市场敞口。消融实验验证了其在多种强化学习算法下的稳健性。嵌入与元学习技术有效应对高维、噪声大、非平稳金融数据的复杂性，同时提升投资组合表现与风险管理能力。
原文摘要 · Abstract (English)
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.
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