论文标题
深层Q网络在投资组合管理中的应用
Application of Deep Q-Network in Portfolio Management
论文作者
论文摘要
机器学习算法和神经网络被广泛应用于许多不同领域，例如股票市场预测，面部识别和人口分析。本文将介绍一项基于经典的深度强化学习算法，深Q网络的策略，用于股票市场的投资组合管理。这是一种通过Q学习优化的深神经网络。为了使DQN适应金融市场，我们首先将行动空间离散为不同资产中投资组合的权重，以便投资组合管理成为深层Q-Network可以解决的问题。接下来，我们将卷积神经网络和决斗Q-NET结合起来，以增强算法的识别能力。在实验上，我们选择了五个低硫化的美国股票来测试该模型。结果表明，基于DQN的策略的表现优于其他十种传统策略。 DQN算法的利润比其他策略的利润高30％。此外，与最大值下降相关的夏普比率表明，用DQN制定的政策风险最低。
Machine Learning algorithms and Neural Networks are widely applied to many different areas such as stock market prediction, face recognition and population analysis. This paper will introduce a strategy based on the classic Deep Reinforcement Learning algorithm, Deep Q-Network, for portfolio management in stock market. It is a type of deep neural network which is optimized by Q Learning. To make the DQN adapt to financial market, we first discretize the action space which is defined as the weight of portfolio in different assets so that portfolio management becomes a problem that Deep Q-Network can solve. Next, we combine the Convolutional Neural Network and dueling Q-net to enhance the recognition ability of the algorithm. Experimentally, we chose five lowrelevant American stocks to test the model. The result demonstrates that the DQN based strategy outperforms the ten other traditional strategies. The profit of DQN algorithm is 30% more than the profit of other strategies. Moreover, the Sharpe ratio associated with Max Drawdown demonstrates that the risk of policy made with DQN is the lowest.
