用深度强化学习优化投资组合，兼顾收益与风险，适应市场波动。
Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization
- 基于PPO算法构建动态投资策略，融合三种风险度量方法。
- 在三个市场阶段表现优异，显著降低极端行情下的下行风险。
- 适合关注风险控制与复杂市场环境的量化投资者。
不确定性下的投资组合优化本质上是多目标决策问题，涉及收益、风险、市场动态及实际约束的复杂交互。现有基于可靠性的优化方法多依赖静态框架，难以捕捉序列决策、尾部风险和交易成本等市场摩擦。为此，我们提出一种用于多目标可靠性投资组合优化的深度强化学习框架（MORP-DRL），联合优化预期收益与下行风险，采用方差、条件风险价值（CVaR）和熵风险价值（EVaR）三种互补风险度量。为建模不确定性与重尾市场行为，资产收益使用GARCH(1,1)、极值理论和t-拷贝依赖结构表示，并通过拟蒙特卡洛模拟生成真实场景。在考虑交易成本与组合边界的实际约束下，采用近端策略优化（PPO）策略，与NSGA-II对比。在十个全球股票指数上跨疫情前、疫情中、疫情后三个市场阶段的实验表明，MORP-DRL实现竞争性风险-收益表现，显著降低市场压力时期的下行风险，并具备高维组合设置下的可扩展性。
原文摘要 · Abstract (English)
Portfolio optimization under uncertainty is inherently a multi-objective decision problem involving complex interactions among return, risk, market dynamics, and practical investment constraints. Existing reliability based portfolio optimization approaches primarily rely on static optimization frameworks and often fail to capture sequential decision making, tail risk, and market frictions such as transaction costs. To address these limitations, we propose a deep reinforcement learning framework for multi-objective reliability based portfolio optimization (MORP-DRL). The proposed framework jointly optimizes expected return and downside risk using three complementary risk measures: variance, Conditional Value-at-Risk (CVaR), and Entropic Value-at-Risk (EVaR). To model uncertainty and heavy-tailed market behavior, asset returns are represented using GARCH(1,1), Extreme Value Theory, and a t-copula dependence structure, while realistic scenarios are generated through quasi-Monte Carlo simulation. A Proximal Policy Optimization (PPO) based strategy is developed under practical constraints including transaction costs and portfolio bounds, and is benchmarked against NSGA-II. Experiments on ten global equity indices across pre-COVID, COVID, and post-COVID market regimes demonstrate that MORP-DRL achieves competitive risk-return performance, reduced downside risk during periods of market stress, and scalability to high-dimensional portfolio settings.
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