英文标题：
《Temporal Relational Ranking for Stock Prediction》
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作者：
Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, Tat-Seng
  Chua
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最新提交年份：
2019
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英文摘要：
  Stock prediction aims to predict the future trends of a stock in order to help investors to make good investment decisions. Traditional solutions for stock prediction are based on time-series models. With the recent success of deep neural networks in modeling sequential data, deep learning has become a promising choice for stock prediction. However, most existing deep learning solutions are not optimized towards the target of investment, i.e., selecting the best stock with the highest expected revenue. Specifically, they typically formulate stock prediction as a classification (to predict stock trend) or a regression problem (to predict stock price). More importantly, they largely treat the stocks as independent of each other. The valuable signal in the rich relations between stocks (or companies), such as two stocks are in the same sector and two companies have a supplier-customer relation, is not considered. In this work, we contribute a new deep learning solution, named Relational Stock Ranking (RSR), for stock prediction. Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner. The key novelty of our work is the proposal of a new component in neural network modeling, named Temporal Graph Convolution, which jointly models the temporal evolution and relation network of stocks. To validate our method, we perform back-testing on the historical data of two stock markets, NYSE and NASDAQ. Extensive experiments demonstrate the superiority of our RSR method. It outperforms state-of-the-art stock prediction solutions achieving an average return ratio of 98% and 71% on NYSE and NASDAQ, respectively.
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中文摘要：
股票预测旨在预测股票的未来趋势，以帮助投资者做出正确的投资决策。股票预测的传统解决方案是基于时间序列模型。随着深度神经网络在序列数据建模方面的成功，深度学习已成为股票预测的一个有希望的选择。然而，大多数现有的深度学习解决方案都没有针对投资目标进行优化，即选择预期收益最高的最佳股票。具体而言，他们通常将股票预测表述为分类（预测股票趋势）或回归问题（预测股票价格）。更重要的是，他们在很大程度上将这些股票视为相互独立的。不考虑股票（或公司）之间丰富关系中的有价值信号，例如两个股票位于同一部门，两个公司有供应商-客户关系。在这项工作中，我们提出了一种新的深度学习解决方案，称为关系股票排名（RSR），用于股票预测。我们的RSR方法在两个主要方面改进了现有的解决方案：1）裁剪股票排名的深度学习模型，2）以时间敏感的方式捕捉股票关系。我们工作的主要创新之处在于提出了神经网络建模中的一个新组件，即时态图卷积，它可以联合建模股票的时态演化和关系网络。为了验证我们的方法，我们对纽约证券交易所和纳斯达克两个股票市场的历史数据进行了回测。大量实验证明了我们的RSR方法的优越性。它的表现优于最先进的股票预测解决方案，在纽约证券交易所和纳斯达克的平均回报率分别为98%和71%。
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computational Engineering, Finance, and Science        计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Information Retrieval        信息检索
分类描述：Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
涵盖索引，字典，检索，内容和分析。大致包括ACM主题课程H.3.0、H.3.1、H.3.2、H.3.3和H.3.4中的材料。
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一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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