英文标题：
《Multi-channel discourse as an indicator for Bitcoin price and volume
  movements》
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作者：
Marvin Aron Kennis
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最新提交年份：
2018
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英文摘要：
  This research aims to identify how Bitcoin-related news publications and online discourse are expressed in Bitcoin exchange movements of price and volume. Being inherently digital, all Bitcoin-related fundamental data (from exchanges, as well as transactional data directly from the blockchain) is available online, something that is not true for traditional businesses or currencies traded on exchanges. This makes Bitcoin an interesting subject for such research, as it enables the mapping of sentiment to fundamental events that might otherwise be inaccessible. Furthermore, Bitcoin discussion largely takes place on online forums and chat channels. In stock trading, the value of sentiment data in trading decisions has been demonstrated numerous times [1] [2] [3], and this research aims to determine whether there is value in such data for Bitcoin trading models. To achieve this, data over the year 2015 has been collected from Bitcointalk.org, (the biggest Bitcoin forum in post volume), established news sources such as Bloomberg and the Wall Street Journal, the complete /r/btc and /r/Bitcoin subreddits, and the bitcoin-otc and bitcoin-dev IRC channels. By analyzing this data on sentiment and volume, we find weak to moderate correlations between forum, news, and Reddit sentiment and movements in price and volume from 1 to 5 days after the sentiment was expressed. A Granger causality test confirms the predictive causality of the sentiment on the daily percentage price and volume movements, and at the same time underscores the predictive causality of market movements on sentiment expressions in online communities
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中文摘要：
本研究旨在确定比特币相关新闻出版物和在线话语如何在比特币交易价格和交易量的变动中表达。由于本身是数字的，所有比特币相关的基础数据（来自交易所，以及直接来自区块链的交易数据）都可以在线获得，这对于在交易所交易的传统企业或货币来说是不真实的。这使得比特币成为此类研究的一个有趣的课题，因为它能够将情绪映射到根本事件，否则这些事件可能无法访问。此外，比特币讨论主要在在线论坛和聊天频道上进行。在股票交易中，情绪数据在交易决策中的价值已经被证明了无数次[1][2][3]，本研究旨在确定这些数据对于比特币交易模型是否有价值。为了实现这一目标，从Bitcointalk收集了2015年的数据。org（博文量最大的比特币论坛），建立了彭博社和《华尔街日报》、complete/r/btc和/r/Bitcoin subreddits以及比特币otc和比特币开发IRC频道等新闻来源。通过分析情绪和成交量数据，我们发现论坛、新闻和Reddit情绪与情绪表达后1至5天内价格和成交量的变动之间存在弱到中等的相关性。格兰杰因果关系检验证实了情绪对每日百分比价格和成交量变动的预测因果关系，同时强调了市场变动对在线社区情绪表达的预测因果关系
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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：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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