摘要翻译：
我们描述了一个新的因果推理框架及其在时间序列返回中的应用。在这个系统中，因果关系被表示为逻辑公式，允许我们以计算有效的方式检验任意复杂的假设。我们使用公因子模型模拟回报时间序列，并表明在此数据上，所描述的方法显著优于格兰杰因果关系（解决这类问题的主要方法）。最后将该方法应用于实际收益率数据，表明该方法能够发现股票之间的新关系。所描述的方法是一种通用的方法，它允许将价格和数量数据与不同时间尺度的定性信息（从利率公告、收益报告到新闻报道）相结合，揭示一些以前看不见的看似相关的价格波动的常见原因。
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英文标题：
《Investigating Causal Relationships in Stock Returns with Temporal Logic
  Based Methods》
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作者：
Samantha Kleinberg, Petter N. Kolm and Bud Mishra
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最新提交年份：
2010
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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：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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英文摘要：
  We describe a new framework for causal inference and its application to return time series. In this system, causal relationships are represented as logical formulas, allowing us to test arbitrarily complex hypotheses in a computationally efficient way. We simulate return time series using a common factor model, and show that on this data the method described significantly outperforms Granger causality (a primary approach to this type of problem). Finally we apply the method to real return data, showing that the method can discover novel relationships between stocks. The approach described is a general one that will allow combination of price and volume data with qualitative information at varying time scales (from interest rate announcements, to earnings reports to news stories) shedding light on some of the previously invisible common causes of seemingly correlated price movements.
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PDF链接：
https://arxiv.org/pdf/1006.1791