用强化学习动态配置全球股市投资组合，提升不确定性下的收益表现。
Deep Reinforcement Learning Framework for Diversified Portfolio Management Across Global Equity Markets
- 基于软演员-评论家算法优化连续权重，融合交易成本与分散化约束。
- 在欧元斯托克50指数上实现显著超额收益，但全市场未达统计显著。
- 适合关注全球资产配置与高波动期策略的投资者参考。
本研究构建并评估了一种用于全球股市动态资产配置的深度强化学习框架。采用软演员-评论家算法，在马尔可夫决策过程中学习连续投资组合权重，将交易成本、换手率惩罚和分散化约束纳入奖励函数。对比了五种模型配置，包括奖励设计、策略结构（平面与分层狄利克雷）、投资组合约束及时间编码器（LSTM与Transformer），通过16轮滚动优化验证，覆盖2003-2026年纳斯达克100、日经225和欧元斯托克50数据。结果表明，强化学习策略在欧元斯托克50上表现优异，出现统计显著的异常收益；但整体假设仅部分成立：所有市场下均未在HAC稳健推断中实现相对于买入持有策略的统计显著超额收益。制度分析显示，强化学习在高不确定时期价值最大；跨市场集成进一步提升了风险调整后表现，证实地理分散化优势。
原文摘要 · Abstract (English)
This study develops and evaluates a deep reinforcement learning framework for dynamic portfolio allocation across global equity markets. The Soft Actor-Critic algorithm is used to learn continuous portfolio weights within a Markov Decision Process, incorporating transaction costs, turnover penalties, and diversification constraints into the reward function. Five model configurations are compared, varying in reward formulation, policy structure (flat versus hierarchical Dirichlet), portfolio constraints, and temporal encoder (LSTM versus Transformer), and evaluated via walk-forward optimization across sixteen out-of-sample folds spanning 2003-2026 on the Nasdaq-100, Nikkei 225, and Euro Stoxx 50. Results show that RL strategies achieve competitive risk-adjusted performance primarily in the Euro Stoxx 50, where statistically significant abnormal returns are observed, but the central hypothesis is only partially confirmed: no strategy achieves statistically significant excess returns relative to Buy and Hold under HAC-robust inference across all markets. Regime analysis reveals that RL adds the most value during periods of elevated uncertainty, while ensemble aggregation across markets improves risk-adjusted performance and confirms the benefits of geographic diversification.
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