基于强化学习框架的投资组合管理

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-05-19 DOI:10.1002/for.3155
Wu Junfeng, Li Yaoming, Tan Wenqing, Chen Yun
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引用次数: 0

摘要

投资组合管理对投资者至关重要。我们提出了一种基于强化学习的动态投资组合管理框架,使用近似策略优化算法。该框架由两部分组成,包括特征提取网络和全连接网络。首先,以往基于强化学习的投资组合管理研究大多针对离散行动空间。我们针对带有约束条件(即组合权重之和等于 1)的连续行动空间问题提出了一种潜在的解决方案。其次,我们探索了不同特征提取网络(即卷积神经网络[CNN]、长短期记忆[LSTM]网络和卷积 LSTM 网络)与系统的结合,并对包括 16 个特征在内的 6 种资产进行了大量实验。实证结果表明,CNN 在测试集中表现最佳。最后,我们讨论了交易频率对交易系统的影响,发现在测试集中,月度交易频率的夏普比率高于其他交易频率。
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Portfolio management based on a reinforcement learning framework

Portfolio management is crucial for investors. We propose a dynamic portfolio management framework based on reinforcement learning using the proximal policy optimization algorithm. The two-part framework includes a feature extraction network and a full connected network. First, the majority of the previous research on portfolio management based on reinforcement learning has been dedicated to discrete action spaces. We propose a potential solution to the problem of a continuous action space with a constraint (i.e., the sum of the portfolio weights is equal to 1). Second, we explore different feature extraction networks (i.e., convolutional neural network [CNN], long short-term memory [LSTM] network, and convolutional LSTM network) combined with our system, and we conduct extensive experiments on the six kinds of assets, including 16 features. The empirical results show that the CNN performs best in the test set. Last, we discuss the effect of the trading frequency on our trading system and find that the monthly trading frequency has a higher Sharpe ratio in the test set than other trading frequencies.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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