Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization
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摘要
This paper compares a policy-gradient Deep Reinforcement Learning (PPO) approach to classical Mean-Variance Optimization (MVO) for multi-asset portfolio allocation on S&P 500 sector indices, using a 60-day lookback, volatility regime features and a Differential Sharpe reward tailored for online learning; across 10 sliding-window backtests (2012–2021) the DRL ensemble achieves materially higher annual returns and Sharpe while reducing turnover and drawdowns versus MVO [page::0][page::5].
