英文标题：
《Information-theoretic measures for non-linear causality detection:
  application to social media sentiment and cryptocurrency prices》
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作者：
Z. Keskin and T. Aste
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最新提交年份：
2019
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英文摘要：
  Information transfer between time series is calculated by using the asymmetric information-theoretic measure known as transfer entropy. Geweke\'s autoregressive formulation of Granger causality is used to find linear transfer entropy, and Schreiber\'s general, non-parametric, information-theoretic formulation is used to detect non-linear transfer entropy.   We first validate these measures against synthetic data. Then we apply these measures to detect causality between social sentiment and cryptocurrency prices. We perform significance tests by comparing the information transfer against a null hypothesis, determined via shuffled time series, and calculate the Z-score. We also investigate different approaches for partitioning in nonparametric density estimation which can improve the significance of results.   Using these techniques on sentiment and price data over a 48-month period to August 2018, for four major cryptocurrencies, namely bitcoin (BTC), ripple (XRP), litecoin (LTC) and ethereum (ETH), we detect significant information transfer, on hourly timescales, in directions of both sentiment to price and of price to sentiment. We report the scale of non-linear causality to be an order of magnitude greater than linear causality.
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中文摘要：
时间序列之间的信息传递是通过使用非对称信息理论度量，即传递熵来计算的。Geweke的Granger因果关系自回归公式用于寻找线性转移熵，Schreiber的一般非参数信息论公式用于检测非线性转移熵。我们首先根据合成数据验证这些度量。然后，我们应用这些措施来检测社会情绪和加密货币价格之间的因果关系。我们通过将信息传递与通过随机时间序列确定的零假设进行比较来进行显著性检验，并计算Z分数。我们还研究了非参数密度估计中的不同划分方法，这可以提高结果的显著性。在截至2018年8月的48个月内，我们对四种主要加密货币，即比特币（BTC）、ripple（XRP）、litecoin（LTC）和以太坊（ETH）的情绪和价格数据使用这些技术，在每小时的时间尺度上检测到情绪对价格和价格对情绪的重要信息传递。我们报告非线性因果关系的规模比线性因果关系大一个数量级。
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分类信息：

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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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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一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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