A new deep reinforcement learning model for dynamic portfolio optimization

Abstract

There are many challenging problems for dynamic portfolio optimization using deep reinforcement learning, such as the high dimensions of the environmental and action spaces, as well as the extraction of useful information from a high-dimensional state space and noisy financial time-series data. To solve these problems, we propose a new model structure called the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method with multi-head attention reinforcement learning. This new model integrates data processing methods, a deep learning model, and a reinforcement learning model to improve the perception and decision-making abilities of investors. Empirical analysis shows that our proposed model structure has some advantages in dynamic portfolio optimization. Moreover, we find an-other robust investment strategy in the process of experimental comparison, where each stock in the portfolio is given the same capital and the structure is applied separately.