基于深度强化学习的动态投资组合优化新模型

A new deep reinforcement learning model for dynamic portfolio optimization

基于深度强化学习的动态投资组合优化模型存在很多具有挑战性的问题,例如高维的环境空间和动作空间,以及如何从高维状态空间和嘈杂的金融时间序列数据中提取有用信息。为了解决这些问题,我们提出了一种新的模型结构,称为自适应噪声的完整集合经验模态分解-多头注意力网络-强化学习模型。这种新模型结构集成了数据处理方法、深度学习模型和强化学习模型,以提高感知和决策能力。实证分析表明,我们提出的模型结构在动态投资组合优化方面具有一定的优势。此外,在实验对比的过程中,我们发现了另外一种稳健的投资策略。该策略为,投资组合中的每只股票给定相同资金,并将独立的结构分别作用于每只股票。

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 another 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.