Researchdeep reinforcement learningportfolio optimizationquantitative finance
DRL Outperforms MVO For Portfolio Optimization
7.1
A Feb 19, 2026 arXiv preprint by Srijan Sood compares model-free deep reinforcement learning (DRL) to Mean-Variance Optimization (MVO) for portfolio allocation. The authors describe practical adjustments for both methods and report backtests where the DRL agent outperforms MVO on Sharpe ratio, maximum drawdown, and absolute returns. Results suggest DRL can be a viable alternative for quantitatively driven portfolio management.
Key Points
- 1Demonstrates model-free deep reinforcement learning achieving superior portfolio allocation versus MVO in backtests
- 2Highlights robustness across Sharpe ratio, maximum drawdown, and absolute return metrics
- 3Suggests practitioners can consider DRL as a practical alternative to traditional MVO workflows
Scoring Rationale
Demonstrates meaningful DRL gains in thorough backtests, but remains a single arXiv preprint lacking peer review.
Sources
Public references used for this report.
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