The Impact and Interactions of Artificial Intelligence Pricing Algorithms in Dealer Market

人工智能定价算法对经销商市场的影响与互动

• 批准号: 2444048
• 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-2444048
• 关键词: Impact; Interactions; Artificial; Intelligence; Pricing

We study the interactions of artificial intelligence pricing algorithms and their impact on market making prices in dealer market. In a dealer market, dealers, also called 'market makers', post prices at which they are willing to buy or sell assets. Bonds and FX spots are primarily traded via dealer market. Nasdaq is a typical example of equity dealer market. We are interested in the case where market makers apply artificial intelligence in algorithmic pricing of financial instruments, with a specific focus on the interactions of these algorithms and their impact on prices whether they could lead to tacit collusion. These algorithms allow market makers to automate pricing by learning from market data through trial and error. As artificial intelligence algorithms are increasingly incorporated in financial industry, there are growing concerns from regulators and market participants whether such interactions of algorithms could result in undesirable outcomes, even though the algorithms are not intended to do so by design. The objective of this research project is to tackle these concerns and provide references for market regulators on the usage of AI algorithms in dealer markets. We start by setting a mathematical framework for market makers in dealer market, and then introducing multi-agent reinforcement learning algorithms for each market maker to set prices through a pricing game. Some preliminary experimental results are achieved under discrete-time settings, and we are working at extending the model to continuous-time, where stochastic control models and multi-agent reinforcement learning will intertwine. We aim at developing theoretical framework to tackle the interactions of AI algorithms in market making. There is amplified literature on algorithmic trading from stochastic control perspective. The topic of algorithmic pricing and AI algorithms have been started only a few years ago, in which recent research focuses on tacit collusion in goods market. However, there is still no existing research on AI pricing algorithms in financial dealer market despite growing focus from regulators. Yet there is no theory explaining the possible interactions of AI pricing algorithms. The research we are working on is therefore novel. This research project falls within the following EPSRC research areas: Artificial intelligence technologies, Non-linear systems, Numerical analysis, Operational Research, Statistics and applied probability. This research project is in collaboration with FX eRisk team in HSBC.

我们研究人工智能定价算法的相互作用及其对经销商市场做市价格的影响。在经销商市场中，经销商（也称为“做市商”）公布他们愿意购买或出售资产的价格。债券和外汇现货主要通过交易商市场进行交易。纳斯达克是股票交易商市场的典型例子。我们对做市商将人工智能应用于金融工具的算法定价的案例感兴趣，特别关注这些算法的相互作用及其对价格的影响是否会导致默契共谋。这些算法允许做市商通过反复试验从市场数据中学习来实现自动化定价。随着人工智能算法越来越多地融入金融行业，监管机构和市场参与者越来越担心算法的这种相互作用是否会导致不良结果，尽管这些算法的设计初衷并非如此。本研究项目的目的就是解决这些问题，并为市场监管机构在经销商市场中使用人工智能算法提供参考。我们首先为经销商市场的做市商设定一个数学框架，然后为每个做市商引入多智能体强化学习算法，通过定价博弈来设定价格。在离散时间设置下取得了一些初步的实验结果，我们正在努力将模型扩展到连续时间，其中随机控制模型和多智能体强化学习将交织在一起。我们的目标是开发理论框架来解决人工智能算法在做市中的相互作用。从随机控制的角度来看，关于算法交易的文献有很多。算法定价和人工智能算法的话题才刚刚开始几年，最近的研究重点是商品市场中的默契合谋。然而，尽管监管机构日益关注，但金融交易商市场中的人工智能定价算法仍然没有研究。然而，还没有任何理论可以解释人工智能定价算法之间可能存在的相互作用。因此，我们正在进行的研究是新颖的。该研究项目属于以下 EPSRC 研究领域：人工智能技术、非线性系统、数值分析、运筹学、统计学和应用概率。该研究项目是与汇丰银行 FX eRisk 团队合作的。