A new deep reinforcement learning model for dynamic portfolio optimization
A new deep reinforcement learning model for dynamic portfolio optimization
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DOI：
10.52396/justc-2022-0072
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Weiwei Zhuang;Cai Chen;Guoxin Qiu
中科院分区：
文献类型：
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作者：
Weiwei Zhuang;Cai Chen;Guoxin Qiu
使用深度强化学习进行动态投资组合优化存在许多挑战性问题，例如环境和行动空间的高维，以及从高维状态空间和噪声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，称为完整的集成经验模式分解与自适应噪声（CEEMDAN）方法与多头注意强化学习。这种新模型集成了数据处理方法、深度学习模型和强化学习模型，以提高投资者的感知和决策能力。实证分析表明，我们提出的模型结构在动态投资组合优化中具有一定的优势。此外，我们发现另一个稳健的投资策略，在实验比较的过程中，在投资组合中的每只股票被赋予相同的资本和结构分别应用。
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.