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

在电子市场中，计算机化算法交易员（ATs）的数量激增。这引发了相当大的政策辩论，即ATs的存在是促进还是阻碍了健康的市场。进一步的政策关切是，应采取什么步骤（如果有的话）来管制自动交易行为，以及在披露自动交易参与情况方面，市场交易所提供者应承担什么责任，或者是否应强迫它们提供不使用自动交易的替代方案。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。我们与有财务动机的人类交易员一起管理资产市场，并以受控的方式介绍AT和有关其存在的信息。不像传统的资产市场，我们控制着，因此知道资产的潜在基本价值是什么，以及每个交易者知道这些信息。此外，我们清楚地知道AT采取了哪些市场行动，以及他们的基本策略。通过外部控制交易者可用的交易所类型，我们得到了进一步的清晰衡量。我们首先评估散户和机构投资者是否反对另类类型的ATs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基本原则。寻求无风险机会获得即时利润的交易所监控多个促进同一资产交易的交易所。他们可以寻找试图制造错误定价的机会，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，我们考察了通过市场订单套利的流动性消耗型ATs和通过限价订单套利的流动性提供型ATs的影响。我们还通过改变人工智能的速度来评估高频交易的影响，在人类可以采取市场行动的速度和那些以低于人类的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，认为人工智能的表现优于人类交易员、因此成为投资者可行的替代选择的一个关键特征是，它们交易速度快。在一个有效的市场中，速度带来了更多的交易错误价格限制订单的机会，它也允许在订单簿中保持专有地位，以有利的价格占领市场。支持这一观点的现有证据来自混合实验/模拟，在混合实验/模拟中，相同数量的人工智能和人类交易员在市场上互动。我们引入了不平衡设计，将人工智能和人类交易员置于市场的对立面，以衡量这是否会影响市场和交易员的总体表现。我们也改变了市场双方的相对市场力量。最后，我们评估了放置暗池市场的影响，在暗池市场中，资产进行双边交易，延迟价格公告，以及公开市场。然后，我们评估了允许掠夺性ATs进入这两个市场的影响。这使我们能够解决当前的监管问题，即暗池市场的私人供应商在维护他们承诺提供的资产安全港方面存在道德风险。

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.