英文标题：
《Predicting future stock market structure by combining social and
  financial network information》
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作者：
Th\\\'arsis T. P. Souza and Tomaso Aste
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最新提交年份：
2018
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英文摘要：
  We demonstrate that future market correlation structure can be predicted with high out-of-sample accuracy using a multiplex network approach that combines information from social media and financial data. Market structure is measured by quantifying the co-movement of asset prices returns, while social structure is measured as the co-movement of social media opinion on those same assets. Predictions are obtained with a simple model that uses link persistence and link formation by triadic closure across both financial and social media layers. Results demonstrate that the proposed model can predict future market structure with up to a 40\\% out-of-sample performance improvement compared to a benchmark model that assumes a time-invariant financial correlation structure. Social media information leads to improved models for all settings tested, particularly in the long-term prediction of financial market structure. Surprisingly, financial market structure exhibited higher predictability than social opinion structure.
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中文摘要：
我们证明，未来的市场相关性结构可以通过综合社交媒体和金融数据信息的多重网络方法进行预测，具有很高的样本外精确度。市场结构是通过量化资产价格收益的联动来衡量的，而社会结构是通过社交媒体对这些相同资产的意见的联动来衡量的。预测是通过一个简单的模型得出的，该模型通过金融和社交媒体层面的三元闭合来使用链接持久性和链接形成。结果表明，与假设财务关联结构时不变的基准模型相比，该模型可以预测未来市场结构，样本外绩效提高了40%。社交媒体信息可以改进所有测试环境的模型，尤其是在金融市场结构的长期预测方面。令人惊讶的是，金融市场结构表现出比社会舆论结构更高的可预测性。
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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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