英文标题：
《Taxable Stock Trading with Deep Reinforcement Learning》
---
作者：
Shan Huang
---
最新提交年份：
2019
---
英文摘要：
In this paper, we propose stock trading based on the average tax basis. Recall that when selling stocks, capital gain should be taxed while capital loss can earn certain tax rebate. We learn the optimal trading strategies with and without considering taxes by reinforcement learning. The result shows that tax ignorance could induce more than 62% loss on the average portfolio returns, implying that taxes should be embedded in the environment of continuous stock trading on AI platforms.
---
中文摘要：
本文提出了基于平均税基的股票交易。回想一下，当出售股票时，资本收益应纳税，而资本损失可以获得一定的退税。通过强化学习，我们学习了考虑税收和不考虑税收的最优交易策略。结果表明，税收忽视会导致平均投资组合收益损失62%以上，这意味着税收应该嵌入到人工智能平台的股票连续交易环境中。
---
分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Trading and Market Microstructure 交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
--
一级分类：Quantitative Finance 数量金融学
二级分类：Mathematical Finance 数学金融学
分类描述：Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法，包括随机、概率和泛函分析、代数、几何和其他方法
--

---
PDF下载：
-->