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Bilateral ESRC/FNR: Experimental Assessment of the Societal Impact of Algorithmic Traders in Asset Markets

双边 ESRC/FNR：资产市场算法交易者社会影响的实验评估

基本信息

批准号：
ES/P011829/1
负责人：
Jason Shachat
金额：
$ 61.53万
依托单位：
Durham University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ES%2FP011829%2F1
关键词：
Bilateral ESRC FNR Experimental Assessment

项目摘要

The has been proliferation of computerized algorithms traders (ATs) in electronic markets. This has generated considerable policy debate on whether the presence of ATs promotes or obstructs healthy markets. Further policy concerns are what steps - if any - should be taken to regulate AT behaviour and what responsibilities should be assign to market exchange providers regarding disclosures of AT participation, or whether they should be compelled to provide AT-free alternatives. Our study generates evidence to inform such policy debates and policy recommendations. We use the methodology of experimental finance to gather this information. We conduct asset markets with financially motivated human traders and introduce AT's and information about their presence in a controlled way. Unlike traditional asset markets we control, and therefore know what the underlying fundamental values of assets are and what information each traders knows about these. Also, we have clear identification of what markets actions are taken by the AT's and their underlying strategy. We get further clean measurements by exogenously controlling the types of exchanges that are available to the traders. We first assess whether retail and institutional investors are averse to having alternative types of ATs participating in the same markets. Alongside this analysis, we also measure the market impacts and wealth distribution effects of introducing AT-free "safe haven" exchanges. We then turn our attention to arbitrage, a fundamental principle that drives market efficiency. AT's that seek riskless opportunities for instantaneous profit monitor multiple exchanges which facilitate the trade of the same asset. They can either look for attempt to create mispricing opportunities in which they purchase the asset in one exchange and sell it at nearly the same time at a higher price in a different exchange. In our experiments, we examine the impact of liquidity consuming ATs who arbitrage through market orders and liquidity providing ATs who arbitrage through limit orders. We also assess the impact of high frequency trading by varying the speed of the ATs between that at which humans can take market actions to those who act at sub-human speed. There is a strong belief in the artificial intelligence and computer science community that a key attribute for ATs to outperform human traders, and therefore be a viable alternative for investors to use, is that they trade fast. In an efficient market speed leads greater opportunity to trade with misprices limit orders, and it also allows on to maintain the proprietary position in the order book to capture market at an advantageous price. The existing evidence supporting this belief comes from hybrid experiments/simulations in which equal numbers of ATs and human traders interact in a market. We introduce unbalanced designs, with ATs and human traders placed on opposite sides of the market, to measure whether this impacts aggregate performance of the market and traders. We also, vary the relative market power of the sides of the markets. Finally we conclude with an assessment of the impact of placing Dark Pool markets, where assets are traded bilaterally with delayed price announcements, alongside public markets. Then we assess the impact on allowing predatory ATs into either of these markets. This allows us to address contemporary regulatory problems in which private providers of Dark Pool markets have a moral hazard in preserving the ATs safe haven they promise to deliver.

在电子市场中，计算机化算法交易者（AT）的数量激增。这就引起了大量的政策辩论，辩论AT的存在是促进还是阻碍健康的市场。进一步的政策关切是，应采取哪些步骤（如果有的话）来规范反倾销行为，以及在披露反倾销参与情况方面应赋予市场交易提供者哪些责任，或者是否应迫使它们提供不含反倾销的替代办法。我们的研究为此类政策辩论和政策建议提供了证据。我们使用实验金融的方法来收集这些信息。我们与金融动机的人类交易者进行资产市场，并以受控的方式介绍AT及其存在的信息。与传统的资产市场不同，我们控制，因此知道资产的基本价值是什么，每个交易者知道这些信息。此外，我们清楚地了解AT采取的市场行动及其基本策略。我们通过外生控制交易者可用的交易所类型来获得进一步的干净测量。我们首先评估散户投资者和机构投资者是否不愿意让其他类型的AT参与同一市场。除了这一分析，我们还衡量了引入无AT“避风港”交易所的市场影响和财富分配效应。然后，我们将注意力转向套利，这是推动市场效率的一个基本原则。AT寻求无风险的即时利润机会，监控多个交易所，促进同一资产的交易。他们可以试图创造错误定价的机会，在一个交易所购买资产，并在几乎同一时间以更高的价格在另一个交易所出售。在我们的实验中，我们研究的影响，流动性消耗AT通过市场订单和流动性提供AT通过限价订单套利套利。我们还通过改变AT的速度来评估高频交易的影响，AT的速度介于人类可以采取市场行动的速度和以低于人类速度行动的速度之间。人工智能和计算机科学界有一种强烈的信念，即AT的一个关键属性优于人类交易员，因此成为投资者使用的可行替代品，就是它们的交易速度快。在一个有效的市场速度导致更大的机会与错误的价格限制订单交易，它也允许在订单簿中保持专有的位置，以捕捉市场在一个有利的价格。支持这一观点的现有证据来自混合实验/模拟，其中相同数量的AT和人类交易员在市场中互动。我们引入了不平衡设计，将AT和人类交易者放置在市场的相对两侧，以衡量这是否会影响市场和交易者的总体表现。我们还改变了市场双方的相对市场力量。最后，我们得出结论，将暗池市场的影响进行评估，其中资产交易的双边延迟价格公告，以及公开市场。然后，我们评估了允许掠夺性AT进入这两个市场的影响。这使我们能够解决当代的监管问题，其中暗池市场的私人提供商在保护他们承诺提供的AT避风港方面存在道德风险。