乐观的牛市或悲观的熊市：股票投资组合配置的自适应深度强化学习
Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation
摘要：
投资组合配置对投资公司至关重要。然而，在复杂多变的股票市场中获得最佳策略是具有挑战性的。在本文中，我们提出了一种新的自适应深度确定性强化学习方案（自适应DDPG）用于投资组合分配任务，该方案结合了乐观或悲观的深度强化学习，反映在预测误差的影响中。选取道琼斯30种成分股作为我们的交易股票，以它们的日价格作为训练和测试数据。我们训练自适应DDPG代理，并获得一个交易策略。将自适应DDPG的性能与普通DDPG、道琼斯工业平均指数以及传统的最小方差和均值-方差投资组合配置策略进行了比较。自适应DDPG在投资回报率和夏普比率方面优于基线。
英文摘要：
Portfolio allocation is crucial for investment companies. However, getting the best strategy in a complex and dynamic stock market is challenging. In this paper, we propose a novel Adaptive Deep Deterministic Reinforcement Learning scheme (Adaptive DDPG) for the portfolio allocation task, which incorporates optimistic or pessimistic deep reinforcement learning that is reflected in the influence from prediction errors. Dow Jones 30 component stocks are selected as our trading stocks and their daily prices are used as the training and testing data. We train the Adaptive DDPG agent and obtain a trading strategy. The Adaptive DDPG's performance is compared with the vanilla DDPG, Dow Jones Industrial Average index and the traditional min-variance and mean-variance portfolio allocation strategies. Adaptive DDPG outperforms the baselines in terms of the investment return and the Sharpe ratio.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
