Risk-Aware Deep Reinforcement Learning for Dynamic Portfolio Optimization

TL;DR

This paper develops a deep reinforcement learning framework for portfolio optimization that incorporates risk measures like drawdown and volatility, aiming to improve risk management in asset allocation.

Contribution

It introduces a novel DRL approach with risk-aware reward functions and direct risk constraints, advancing the application of RL in financial portfolio management.

Findings

DRL stabilizes portfolio volatility effectively.

Risk-adjusted returns decrease due to conservative policies.

Highlights the importance of reward shaping for better performance.

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.