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Myopic Optimality: why reinforcement learning portfolio management strategies lose money

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  • Yuming Ma

Abstract

Myopic optimization (MO) outperforms reinforcement learning (RL) in portfolio management: RL yields lower or negative returns, higher variance, larger costs, heavier CVaR, lower profitability, and greater model risk. We model execution/liquidation frictions with mark-to-market accounting. Using Malliavin calculus (Clark-Ocone/BEL), we derive policy gradients and risk shadow price, unifying HJB and KKT. This gives dual gap and convergence results: geometric MO vs. RL floors. We quantify phantom profit in RL via Malliavin policy-gradient contamination analysis and define a control-affects-dynamics (CAD) premium of RL indicating plausibly positive.

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  • Yuming Ma, 2025. "Myopic Optimality: why reinforcement learning portfolio management strategies lose money," Papers 2509.12764, arXiv.org.
    • Handle: RePEc:arx:papers:2509.12764
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