Dynamic graph reinforcement learning algorithm for portfolio management: A novel time–frequency correlated model

Abstract

Revealing the dynamic correlations among various assets is crucial for portfolio management. In this study, we build a novel Multi-graphs representation based on wavelet coherence to capture and learn their dynamic time–frequency correlations. Then, a novel portfolio management strategy is proposed by integrating the Multi-graphs representation with the deep reinforcement learning algorithm, referred to as the Dynamic Wavelet Coherence Graph Convolutional Reinforcement Learning (WCG-RL) algorithm. Several numerical experiments fully illustrate the performance of our proposed WCG-RL algorithm is applicable to stocks with different market capitalization, and its performance surpasses that of the state-of-the-art algorithms in the Chinese stock market.