Market Making with informed traders

与消息灵通的交易者一起做市

• 批准号: 2442015
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2442015
• 关键词: Market; Making; informed; traders

Nowadays, many types of financial contracts are traded in electronic markets. When designing algorithmic trading strategies to act in these exchanges it is important to understand the different types of trading behaviour one will encounter. An important type of market participant is the market maker, sometimes also called dealer. Her role is to facilitate trade and provide liquidity to the exchange. She does provide liquidity by posting orders on both the bid and the ask side. Typically, market makers make money by earning the spread between bid and ask prices. In addition, depending on the market venue dealers might earn a fixed/variable rebate for providing liquidity.The optimal market making problem has been extensively studied over the years and it has been tackled with different approaches. Until recently, stochastic control is one of the widely used techniques. Lately, Machine Learning techniques have been used to find optimal market making solutions and the role of Artificial Intelligence in this field is rapidly expanding.There are two main sources of risk that a dealer might face which have been identified in the literature. The first one is called inventory risk and arises from the fact that most of the time the dealer's inventory is not zero and it is therefore exposed to changes in the asset's valuation. The second important risk factor is called adverse selection and arises from the fact that among the crowd of market participants there are traders which have private information and try to exploit it at the dealer's expenses.Our research project focuses on the adverse selection problem and on how to identify informed traders among market participants. Order flow is said to be toxic when it adversely selects market makers, who might not know that they are providing liquidity at a loss. Flow toxicity has been studied extensively and researchers have provided over the years two different ways of measuring it, which however have been criticised and are object of a dispute. Our main goal is to develop a market making model where the dealer makes use of Deep Reinforcement Learning techniques to detect informed traders and adjust her quotes according to the order flow. Typical market making models assume that the dealer does not use her accumulated experience to detect patterns in participants' behaviour. Indeed, she could use her memory of the past to adjust her quotes and protect herself from exogenous information that she might not be aware of.Our research should improve the quality of liquidity provision for both the market maker and clients which do not have any private information. Indeed, knowing that there are traders who exploit private information might help the dealer to adjust her quotes and not to trade at a loss too often. Another interesting problem that we would like to tackle is the broker perspective. A broker act in the same way as a market maker but he can decide who she wants to trade with. She could even decide arbitrarily not to trade with informed clients, and this would result in more fair quotes for other clients, who are normally trading for exogenous reasons. This however gives rise to a trade-off between losing money and finding out private information. Indeed, informed traders might be our only source of information.This project falls within the following EPSRC areas: 1) artificial intelligence technologies2) non-linear systems and 3) statistics and applied probability research area. The industry partner/collaborator for this project is BNP Paribas.

如今，许多类型的金融合同在电子市场上进行交易。在设计算法交易策略以在这些交易所采取行动时，重要的是了解人们将遇到的不同类型的交易行为。市场参与者的一种重要类型是做市商，有时也称为交易商。她的角色是促进贸易，并为交易所提供流动性。她确实通过在买入方和卖出方发布订单来提供流动性。通常，做市商通过赚取买入价和卖出价之间的价差来赚钱。此外，交易商可能会因提供流动资金而获得固定/可变的回扣，视乎市场地点而定。多年来，最优做市问题已被广泛研究，并以不同的方法解决。直到最近，随机控制仍是被广泛使用的技术之一。最近，机器学习技术被用来寻找最优的做市解决方案，人工智能在这一领域的作用正在迅速扩大。文献中已经确定了交易商可能面临的两个主要风险来源。第一种风险称为库存风险，因为交易商的库存在大多数情况下不是零，因此容易受到资产估值变化的影响。第二个重要的风险因素被称为逆向选择，它源于这样一个事实，即在一群市场参与者中，有交易者拥有私人信息，并试图以交易商的费用利用这些信息。我们的研究项目专注于逆向选择问题，以及如何在市场参与者中识别知情的交易者。当订单流不利地选择做市商时，它被认为是有毒的，这些做市商可能不知道他们在亏本地提供流动性。多年来，人们对水流毒性进行了广泛的研究，研究人员提供了两种不同的测量方法，但这两种方法受到了批评，并成为争议的对象。我们的主要目标是开发一个做市模型，交易商利用深度强化学习技术来发现知情的交易者，并根据订单流调整她的报价。典型的做市模型假定，交易商不会利用自己积累的经验来发现参与者的行为模式。事实上，她可以利用对过去的记忆来调整自己的报价，保护自己不受她可能不知道的外部信息的影响。我们的研究应该会改善为没有任何私人信息的做市商和客户提供流动性的质量。事实上，知道有交易员利用私人信息可能有助于交易商调整她的报价，而不是过于频繁地亏损交易。我们想要解决的另一个有趣的问题是经纪人的观点。经纪人的行为与做市商相同，但他可以决定她想要与谁进行交易。她甚至可以武断地决定不与知情客户进行交易，这将为其他客户带来更公平的报价，这些客户通常是出于外部原因进行交易。然而，这导致了在赔钱和寻找私人信息之间的权衡。事实上，消息灵通的交易员可能是我们唯一的信息来源。这个项目属于以下EPSRC领域：1)人工智能技术2)非线性系统和3)统计和应用概率研究领域。该项目的行业合作伙伴/合作者是法国巴黎银行。