英文标题：
《Deep Stock Representation Learning: From Candlestick Charts to
  Investment Decisions》
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作者：
Guosheng Hu and Yuxin Hu and Kai Yang and Zehao Yu and Flood Sung and
  Zhihong Zhang and Fei Xie and Jianguo Liu and Neil Robertson and Timothy
  Hospedales and Qiangwei Miemie
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最新提交年份：
2018
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英文摘要：
  We propose a novel investment decision strategy (IDS) based on deep learning. The performance of many IDSs is affected by stock similarity. Most existing stock similarity measurements have the problems: (a) The linear nature of many measurements cannot capture nonlinear stock dynamics; (b) The estimation of many similarity metrics (e.g. covariance) needs very long period historic data (e.g. 3K days) which cannot represent current market effectively; (c) They cannot capture translation-invariance. To solve these problems, we apply Convolutional AutoEncoder to learn a stock representation, based on which we propose a novel portfolio construction strategy by: (i) using the deeply learned representation and modularity optimisation to cluster stocks and identify diverse sectors, (ii) picking stocks within each cluster according to their Sharpe ratio (Sharpe 1994). Overall this strategy provides low-risk high-return portfolios. We use the Financial Times Stock Exchange 100 Index (FTSE 100) data for evaluation. Results show our portfolio outperforms FTSE 100 index and many well known funds in terms of total return in 2000 trading days.
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中文摘要：
提出了一种基于深度学习的投资决策策略。许多智能决策支持系统的性能都受到股票相似性的影响。现有的大多数股票相似性度量都存在以下问题：（a）许多度量的线性性质无法捕捉非线性股票动态；（b） 许多相似性度量（如协方差）的估计需要很长的历史数据（如3K天），不能有效地代表当前市场；（c） 它们无法捕获平移不变性。为了解决这些问题，我们应用卷积自动编码器学习股票表示，在此基础上，我们提出了一种新的投资组合构建策略：（i）使用深入学习的表示和模块化优化对股票进行聚类并识别不同的部门，（ii）根据夏普比率在每个集群内挑选股票（夏普1994）。总体而言，该策略提供低风险高回报投资组合。我们使用英国《金融时报》股票交易所100指数（FTSE 100）数据进行评估。结果显示，我们的投资组合在2000个交易日的总回报率方面优于富时100指数和许多知名基金。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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