摘要翻译：
基于在线情绪跟踪的金融市场预测近年来受到广泛关注。然而，这一新兴领域的大多数结果依赖于数据集和情感跟踪工具的独特组合。这使得人们很难从网上情绪和市场价值之间的明显关系中消除测量和工具的影响。在本文中，我们调查了一系列在线数据集（Twitter提要、新闻标题和谷歌搜索查询量）和情绪跟踪方法（Twitter投资者情绪、负面新闻情绪和Tweet&谷歌金融术语搜索量），并比较了它们对道琼斯工业平均指数、交易量、市场波动率(VIX)以及黄金价格等市场指数的财务预测价值。我们还比较了传统投资者情绪调查数据，即投资者情报和每日情绪指数，与上述一套网络情绪指标的预测能力。我们的结果表明，传统的投资者情报调查是金融市场的滞后指标。然而，Google Insight每周关于金融搜索查询的搜索量确实具有预测价值。Twitter投资者情绪的一个指标和Twitter上金融术语在过去1-2天内的出现频率也被发现是每日市场日志回报的非常显著的预测因素。然而，调查发现，一旦我们控制了所有其他情绪指标以及波动率指数，情绪指标对金融市场价值的预测并不具有统计学意义。
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英文标题：
《Predicting Financial Markets: Comparing Survey, News, Twitter and Search
  Engine Data》
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作者：
Huina Mao, Scott Counts and Johan Bollen
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最新提交年份：
2011
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分类信息：

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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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：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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英文摘要：
  Financial market prediction on the basis of online sentiment tracking has drawn a lot of attention recently. However, most results in this emerging domain rely on a unique, particular combination of data sets and sentiment tracking tools. This makes it difficult to disambiguate measurement and instrument effects from factors that are actually involved in the apparent relation between online sentiment and market values. In this paper, we survey a range of online data sets (Twitter feeds, news headlines, and volumes of Google search queries) and sentiment tracking methods (Twitter Investor Sentiment, Negative News Sentiment and Tweet & Google Search volumes of financial terms), and compare their value for financial prediction of market indices such as the Dow Jones Industrial Average, trading volumes, and market volatility (VIX), as well as gold prices. We also compare the predictive power of traditional investor sentiment survey data, i.e. Investor Intelligence and Daily Sentiment Index, against those of the mentioned set of online sentiment indicators. Our results show that traditional surveys of Investor Intelligence are lagging indicators of the financial markets. However, weekly Google Insight Search volumes on financial search queries do have predictive value. An indicator of Twitter Investor Sentiment and the frequency of occurrence of financial terms on Twitter in the previous 1-2 days are also found to be very statistically significant predictors of daily market log return. Survey sentiment indicators are however found not to be statistically significant predictors of financial market values, once we control for all other mood indicators as well as the VIX.
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PDF链接：
https://arxiv.org/pdf/1112.1051