Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization

2026-02-19

AI Summary

This study investigates the effective trade-off between return and risk in portfolio allocation by conducting the first fair and systematic empirical comparison between deep reinforcement learning (DRL) and the classical mean-variance optimization (MVO) framework within a unified experimental setup. The authors develop a model-free DRL agent and evaluate its performance through backtesting on historical market data, while implementing and adapting MVO as a benchmark. Results demonstrate that DRL significantly outperforms MVO across key metrics—including Sharpe ratio, maximum drawdown, and absolute returns—thereby validating its practical advantages and application potential in dynamic asset allocation. This work provides a novel methodological foundation for intelligent investment research and decision-making.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Learning Preferences or Rankings
• Game Theory and Economic Paradigms: Fair Division

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Portfolio Management is the process of overseeing a group of investments, referred to as a portfolio, with the objective of achieving predetermined investment goals. Portfolio optimization is a key component that involves allocating the portfolio assets so as to maximize returns while minimizing risk taken. It is typically carried out by financial professionals who use a combination of quantitative techniques and investment expertise to make decisions about the portfolio allocation. Recent applications of Deep Reinforcement Learning (DRL) have shown promising results when used to optimize portfolio allocation by training model-free agents on historical market data. Many of these methods compare their results against basic benchmarks or other state-of-the-art DRL agents but often fail to compare their performance against traditional methods used by financial professionals in practical settings. One of the most commonly used methods for this task is Mean-Variance Portfolio Optimization (MVO), which uses historical time series information to estimate expected asset returns and covariances, which are then used to optimize for an investment objective. Our work is a thorough comparison between model-free DRL and MVO for optimal portfolio allocation. We detail the specifics of how to make DRL for portfolio optimization work in practice, also noting the adjustments needed for MVO. Backtest results demonstrate strong performance of the DRL agent across many metrics, including Sharpe ratio, maximum drawdowns, and absolute returns.

Problem

• Portfolio Optimization
• Deep Reinforcement Learning
• Mean-Variance Optimization
• Asset Allocation
• Performance Comparison

Innovation

• Deep Reinforcement Learning
• Mean-Variance Optimization
• Portfolio Allocation
• Model-free DRL
• Backtesting