英文标题：
《Bull Bear Balance: A Cluster Analysis of Socially Informed Financial
Volatility》
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作者：
Jonathan Manfield, Derek Lukacsko and Th\\\'arsis T. P. Souza
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最新提交年份：
2018
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英文摘要：
Using a method rooted in information theory, we present results that have identified a large set of stocks for which social media can be informative regarding financial volatility. By clustering stocks based on the joint feature sets of social and financial variables, our research provides an important contribution by characterizing the conditions in which social media signals can lead financial volatility. The results indicate that social media is most informative about financial market volatility when the ratio of bullish to bearish sentiment is high, even when the number of messages is low. The robustness of these findings is verified across 500 stocks from both NYSE and NASDAQ exchanges. The reported results are reproducible via an open-source library for social-financial analysis made freely available.
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中文摘要：
使用一种植根于信息理论的方法，我们给出的结果表明，社交媒体可以为大量股票提供有关金融波动的信息。通过基于社会和金融变量的联合特征集对股票进行聚类，我们的研究通过描述社交媒体信号可能导致金融波动的条件提供了重要贡献。结果表明，当牛市情绪与熊市情绪的比率较高时，即使消息数量较低，社交媒体对金融市场波动的信息也最多。纽约证交所和纳斯达克交易所的500只股票验证了这些发现的稳健性。报告的结果可通过免费提供的社会财务分析开源图书馆复制。
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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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一级分类：Physics 物理学
二级分类：Data Analysis, Statistics and Probability 数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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