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

点击翻译按钮获取中文摘要

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.