英文标题：
《Using Stock Prices as Ground Truth in Sentiment Analysis to Generate
Profitable Trading Signals》
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作者：
Ellie Birbeck and Dave Cliff
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最新提交年份：
2018
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英文摘要：
The increasing availability of \"big\" (large volume) social media data has motivated a great deal of research in applying sentiment analysis to predict the movement of prices within financial markets. Previous work in this field investigates how the true sentiment of text (i.e. positive or negative opinions) can be used for financial predictions, based on the assumption that sentiments expressed online are representative of the true market sentiment. Here we consider the converse idea, that using the stock price as the ground-truth in the system may be a better indication of sentiment. Tweets are labelled as Buy or Sell dependent on whether the stock price discussed rose or fell over the following hour, and from this, stock-specific dictionaries are built for individual companies. A Bayesian classifier is used to generate stock predictions, which are input to an automated trading algorithm. Placing 468 trades over a 1 month period yields a return rate of 5.18%, which annualises to approximately 83% per annum. This approach performs significantly better than random chance and outperforms two baseline sentiment analysis methods tested.
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中文摘要：
“大”（大容量）社交媒体数据的日益可用性激发了大量研究，将情绪分析应用于预测金融市场内的价格变动。该领域之前的工作基于在线表达的情绪代表真实的市场情绪的假设，研究如何将文本的真实情绪（即积极或消极的意见）用于财务预测。在这里，我们考虑的是相反的想法，即在系统中使用股票价格作为基本事实可能是更好的情绪指示。推特被标记为买入或卖出，这取决于股价在接下来的一小时内是上涨还是下跌，并由此为个别公司构建了特定于股票的词典。贝叶斯分类器用于生成股票预测，并将其输入到自动交易算法中。在一个月内完成468笔交易，收益率为5.18%，折合成年率约为83%。该方法的性能明显优于随机机会，并优于测试的两种基线情绪分析方法。
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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：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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