英文标题：
《Learning Unfair Trading: a Market Manipulation Analysis From the
Reinforcement Learning Perspective》
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作者：
Enrique Mart\\\'inez-Miranda and Peter McBurney and Matthew J. Howard
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最新提交年份：
2015
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英文摘要：
Market manipulation is a strategy used by traders to alter the price of financial securities. One type of manipulation is based on the process of buying or selling assets by using several trading strategies, among them spoofing is a popular strategy and is considered illegal by market regulators. Some promising tools have been developed to detect manipulation, but cases can still be found in the markets. In this paper we model spoofing and pinging trading, two strategies that differ in the legal background but share the same elemental concept of market manipulation. We use a reinforcement learning framework within the full and partial observability of Markov decision processes and analyse the underlying behaviour of the manipulators by finding the causes of what encourages the traders to perform fraudulent activities. This reveals procedures to counter the problem that may be helpful to market regulators as our model predicts the activity of spoofers.
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中文摘要：
市场操纵是交易员用来改变金融证券价格的一种策略。一种操纵是基于使用多种交易策略买卖资产的过程，其中欺骗是一种流行的策略，被市场监管机构视为非法。一些有前途的工具已经被开发出来用来检测操纵行为，但在市场上仍然可以找到案例。在本文中，我们对欺骗和ping交易进行了建模，这两种策略的法律背景不同，但市场操纵的基本概念相同。我们在马尔可夫决策过程的完全和部分可观测性范围内使用强化学习框架，通过找出鼓励交易者进行欺诈活动的原因，分析操纵者的潜在行为。这揭示了应对问题的程序，这可能有助于市场监管机构，因为我们的模型预测了欺骗者的活动。
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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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