英文标题：
《Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement
  Learning for Stock Portfolio Allocation》
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作者：
Xinyi Li, Yinchuan Li, Yuancheng Zhan, Xiao-Yang Liu
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最新提交年份：
2019
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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.
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中文摘要：
投资组合分配对投资公司至关重要。然而，在一个复杂而动态的股票市场中获得最佳策略是一项挑战。在本文中，我们针对投资组合分配任务提出了一种新的自适应深度确定性强化学习方案（Adaptive DDPG），该方案融合了乐观或悲观的深度强化学习，反映在预测误差的影响上。我们选择道琼斯30成分股作为交易股票，并将其每日价格作为培训和测试数据。我们训练了自适应DDPG代理，并获得了一个交易策略。将自适应DDPG的性能与香草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分配策略进行了比较。自适应DDPG在投资回报和夏普比率方面优于基线。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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