A Deep Reinforcement Learning Framework for Dynamic Portfolio Optimization: Evidence from China's Stock Market

TL;DR

This paper introduces a novel deep reinforcement learning framework for dynamic portfolio optimization in China's stock market, improving asset allocation and risk management over traditional methods.

Contribution

It develops a new Sharpe ratio-based reward function and an integrated deep reinforcement learning approach with image-based neural networks for financial data processing.

Findings

Model achieves higher Sharpe ratios than benchmarks

Backtesting shows improved risk-adjusted returns

Framework effectively adapts to market dynamics

Abstract

Artificial intelligence is transforming financial investment decision-making frameworks, with deep reinforcement learning demonstrating substantial potential in robo-advisory applications. This paper addresses the limitations of traditional portfolio optimization methods in dynamic asset weight adjustment through the development of a deep reinforcement learning-based dynamic optimization model grounded in practical trading processes. The research advances two key innovations: first, the introduction of a novel Sharpe ratio reward function engineered for Actor-Critic deep reinforcement learning algorithms, which ensures stable convergence during training while consistently achieving positive average Sharpe ratios; second, the development of an innovative comprehensive approach to portfolio optimization utilizing deep reinforcement learning, which significantly enhances model optimization capability through the integration of random sampling strategies during training with image-based deep neural network architectures for multi-dimensional financial time series data processing, average Sharpe ratio reward functions, and deep reinforcement learning algorithms. The empirical analysis validates the model using randomly selected constituent stocks from the CSI 300 Index, benchmarking against established financial econometric optimization models. Backtesting results demonstrate the model's efficacy in optimizing portfolio allocation and mitigating investment risk, yielding superior comprehensive performance metrics.