论文标题
地图：基于多机构学习的投资组合管理系统
MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System
论文作者
论文摘要
使用先进的深度学习方法在股票市场中生成投资策略最近一直是一个令人感兴趣的话题。大多数现有的深度学习方法着重于提出最佳模型或网络体系结构，通过最大化回报。但是，这些模型通常无法考虑并适应不断变化的市场状况。在本文中，我们提出了基于多机构的增强学习的投资组合管理系统（MAP）。地图是一个合作系统，每个代理都是独立的“投资者”创建自己的投资组合的系统。在培训程序中，每个代理都被指导尽可能多样化，同时通过精心设计的损失功能最大化自己的回报。结果，作为系统的地图最终以多元化的投资组合。美国市场数据的12年结果表明，在夏普比率方面，映射的大多数基线都优于大多数基线。此外，我们的结果表明，在我们的系统中增加更多的代理将使我们通过使用更多样化的投资组合来降低风险来获得更高的夏普比率。
Generating an investment strategy using advanced deep learning methods in stock markets has recently been a topic of interest. Most existing deep learning methods focus on proposing an optimal model or network architecture by maximizing return. However, these models often fail to consider and adapt to the continuously changing market conditions. In this paper, we propose the Multi-Agent reinforcement learning-based Portfolio management System (MAPS). MAPS is a cooperative system in which each agent is an independent "investor" creating its own portfolio. In the training procedure, each agent is guided to act as diversely as possible while maximizing its own return with a carefully designed loss function. As a result, MAPS as a system ends up with a diversified portfolio. Experiment results with 12 years of US market data show that MAPS outperforms most of the baselines in terms of Sharpe ratio. Furthermore, our results show that adding more agents to our system would allow us to get a higher Sharpe ratio by lowering risk with a more diversified portfolio.
