Risk-Aware Deep Reinforcement Learning for Dynamic Portfolio Optimization

ArXiv ID: 2511.11481 “View on arXiv”

Authors: Emmanuel Lwele, Sabuni Emmanuel, Sitali Gabriel Sitali

Abstract

This paper presents a deep reinforcement learning (DRL) framework for dynamic portfolio optimization under market uncertainty and risk. The proposed model integrates a Sharpe ratio-based reward function with direct risk control mechanisms, including maximum drawdown and volatility constraints. Proximal Policy Optimization (PPO) is employed to learn adaptive asset allocation strategies over historical financial time series. Model performance is benchmarked against mean-variance and equal-weight portfolio strategies using backtesting on high-performing equities. Results indicate that the DRL agent stabilizes volatility successfully but suffers from degraded risk-adjusted returns due to over-conservative policy convergence, highlighting the challenge of balancing exploration, return maximization, and risk mitigation. The study underscores the need for improved reward shaping and hybrid risk-aware strategies to enhance the practical deployment of DRL-based portfolio allocation models.

Keywords: Deep Reinforcement Learning (DRL), Proximal Policy Optimization (PPO), Sharpe ratio reward function, Maximum drawdown constraint, Portfolio optimization, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics including gradient ascent derivations for Sharpe ratio optimization and constraints via softmax, while demonstrating empirical rigor with backtesting on historical data against benchmarks and risk metrics like maximum drawdown.

  flowchart TD
    A["Research Goal: DRL for Dynamic Portfolio Optimization"] --> B["Methodology: PPO with Sharpe Reward & Risk Constraints"]
    B --> C["Data: Historical High-Performing Equities"]
    C --> D{"Computational Process"}
    D --> E["Training Agent"]
    D --> F["Backtesting"]
    E --> G["Over-conservative Policy Convergence"]
    F --> H["Benchmark vs. Mean-Variance & Equal-Weight"]
    G & H --> I["Outcomes"]
    I --> J["Stable Volatility"]
    I --> K["Degraded Risk-Adjusted Returns"]
    I --> L["Need for Improved Reward Shaping"]