投资组合管理中的DDPG算法

2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2020-10-01 DOI:10.1109/DCABES50732.2020.00065

Fang Lin, Meiqing Wang, Rong Liu, Qianying Hong

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引用次数: 0

摘要

投资组合管理的目的是选择多种金融产品组成一个投资组合，然后对这些投资组合进行管理，以达到分散风险和提高效率的目的。本文将深度确定性策略梯度(Deep Deterministic Policy Gradient, DDPG)算法与神经网络相结合，提出了新的状态、动作和奖励函数。实证分析表明，本文方法优于q -学习算法投资方法、等权重法投资方法、全部资金投资于无风险资产投资方法、全部资金投资于股票投资方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A DDPG Algorithm for Portfolio Management

The purpose of portfolio management is to select a variety of financial products to form a portfolio and then manage these portfolios to achieve the purpose of diversifying risk and improving efficiency. In this paper, the Deep Deterministic Policy Gradient (DDPG) algorithm with neural networks is used, new states, actions and reward functions are proposed. The empirical analysis shows that this paper's method performs better than the method of investing with Q-learning algorithm, equally-weighted method, investing all funds in risk-free assets, or investing all funds in stocks.

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来源期刊

2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

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