英文标题：
《The Anatomy of a Cryptocurrency Pump-and-Dump Scheme》
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作者：
Jiahua Xu, Benjamin Livshits
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最新提交年份：
2019
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英文摘要：
  While pump-and-dump schemes have attracted the attention of cryptocurrency observers and regulators alike, this paper represents the first detailed empirical query of pump-and-dump activities in cryptocurrency markets. We present a case study of a recent pump-and-dump event, investigate 412 pump-and-dump activities organized in Telegram channels from June 17, 2018 to February 26, 2019, and discover patterns in crypto-markets associated with pump-and-dump schemes. We then build a model that predicts the pump likelihood of all coins listed in a crypto-exchange prior to a pump. The model exhibits high precision as well as robustness, and can be used to create a simple, yet very effective trading strategy, which we empirically demonstrate can generate a return as high as 60% on small retail investments within a span of two and half months. The study provides a proof of concept for strategic crypto-trading and sheds light on the application of machine learning for crime detection.
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中文摘要：
虽然pump和dump方案吸引了加密货币观察家和监管机构的关注，但本文首次对加密货币市场中的pump和dump活动进行了详细的实证研究。我们介绍了最近一次抽水和倾倒事件的案例研究，调查了2018年6月17日至2019年2月26日期间在电报渠道组织的412次抽水和倾倒活动，并发现了与抽水和倾倒方案相关的加密市场模式。然后，我们建立了一个模型，预测在加密交易中列出的所有硬币在注入之前的注入可能性。该模型具有较高的精度和稳健性，可用于创建简单但非常有效的交易策略，我们的经验表明，该策略可在两个半月的时间内为小型零售投资带来高达60%的回报。该研究为战略密码交易提供了概念证明，并为机器学习在犯罪检测中的应用提供了启示。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Trading and Market Microstructure        交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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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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