英文标题：
《AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy
  using Interpretable Deep Reinforcement Attention Networks》
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作者：
Jingyuan Wang, Yang Zhang, Ke Tang, Junjie Wu and Zhang Xiong
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最新提交年份：
2019
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英文摘要：
  Recent years have witnessed the successful marriage of finance innovations and AI techniques in various finance applications including quantitative trading (QT). Despite great research efforts devoted to leveraging deep learning (DL) methods for building better QT strategies, existing studies still face serious challenges especially from the side of finance, such as the balance of risk and return, the resistance to extreme loss, and the interpretability of strategies, which limit the application of DL-based strategies in real-life financial markets. In this work, we propose AlphaStock, a novel reinforcement learning (RL) based investment strategy enhanced by interpretable deep attention networks, to address the above challenges. Our main contributions are summarized as follows: i) We integrate deep attention networks with a Sharpe ratio-oriented reinforcement learning framework to achieve a risk-return balanced investment strategy; ii) We suggest modeling interrelationships among assets to avoid selection bias and develop a cross-asset attention mechanism; iii) To our best knowledge, this work is among the first to offer an interpretable investment strategy using deep reinforcement learning models. The experiments on long-periodic U.S. and Chinese markets demonstrate the effectiveness and robustness of AlphaStock over diverse market states. It turns out that AlphaStock tends to select the stocks as winners with high long-term growth, low volatility, high intrinsic value, and being undervalued recently.
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中文摘要：
近年来，金融创新和人工智能技术在各种金融应用中成功结合，包括定量交易（QT）。尽管有大量研究致力于利用深度学习（DL）方法构建更好的QT策略，但现有研究仍然面临着严峻的挑战，尤其是金融方面的挑战，如风险与回报的平衡、对极端损失的抵抗力以及策略的可解释性，这限制了基于DL的策略在现实金融市场中的应用。在这项工作中，我们提出了AlphaStock，一种新的基于强化学习（RL）的投资策略，通过可解释的深度注意网络来增强，以应对上述挑战。我们的主要贡献总结如下：i）我们将深度注意网络与夏普比率导向的强化学习框架相结合，以实现风险回报平衡的投资策略；ii）我们建议对资产之间的相互关系进行建模，以避免选择偏差，并开发跨资产注意机制；iii）据我们所知，这项工作是最早利用深度强化学习模型提供可解释的投资策略的工作之一。在长周期美国和中国市场上的实验证明了AlphaStock在不同市场状态下的有效性和稳健性。事实证明，AlphaStock倾向于选择那些长期增长率高、波动性低、内在价值高、最近被低估的股票作为赢家。
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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
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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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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