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Experiments on the effects of trading algorithms on financial markets

交易算法对金融市场影响的实验

基本信息

批准号：
ES/T006048/1
负责人：
Christoph Siemroth
金额：
$ 27万
依托单位：
University of Essex
依托单位国家：
英国
项目类别：
Research Grant
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=ES%2FT006048%2F1
关键词：
Experiments effects trading algorithms financial

项目摘要

Algorithmic trading, where computers rather than humans execute trades within milliseconds of receiving information, has been on the rise for years. Some authors estimate that 70% of trades in the US are executed by algorithms (Swinburne 2010), with others going up to 85% (Glantz & Kissel 2013). Algorithmic trading has thus had a significant impact on all developed financial markets, but its effects are not yet well recognised; an improved understanding of this relatively new and shifting phenomenon is essential to inform policy debates, such as whether algorithmic trading needs to comply with stricter rules to prevent excess volatility or the formation of asset bubbles. The aim of this project is to answer fundamental questions on the effects of algorithmic trading on financial markets; specifically, whether algorithms trained by state-of-the-art machine learning techniques increase or decrease the likelihood of asset bubbles, whether they increase or decrease volatility and risk in markets, and indeed how such algorithms look like in the first place. That latter question is simple yet not easy to answer, because successful trading algorithms are valuable and therefore kept confidential for fear of them being copied, exploited, or regulated. Another difficulty is that causal effects are notoriously difficult to establish with field data from financial markets, in part because it is typically not even determinable which trades are executed by algorithms in anonymous markets.For these reasons, I will answer these questions with laboratory experiments, where I let human traders and algorithmic traders interact in small lab markets. The advantage of the method is that causal effects can be convincingly established: A control group with only human traders can be compared to a treatment group with algorithmic traders along various outcome measures such as bubble frequency, volatility/risk, or liquidity/trading speed, all else equal. Moreover, every action is observable in the lab, so specific trades can be classified as being initiated by algorithms or humans. It also allows me to investigate which kinds of algorithms evolve when learning against human traders, or against other algorithms in the market.One class of market used in the lab will let subjects/algorithms trade multiple units of a finanical asset. This asset pays out a dividend to the holder at the end of each of 15 rounds. Because there are fewer dividend payoffs remaining in later rounds, the fundamental value of the asset is decreasing over time. Yet in previous experiments with human traders, financial bubbles frequently arose where prices increased dramatically despite the decreasing asset value, which is the bubble pattern also observed in stock or housing markets. This experimental market setting is therefore useful to study bubble formation and causes of bubbles, but so far there is no research on the effect of algorithmic trading in this context.In another class of experimental market, I will introduce news about the value of assets during financial market trading, and observe how traders and algorithms respond to such news shocks. In particular, the main question is whether a sufficient number of trend-following algorithms can lead to drastic drops or increases in prices, thus increasing volatility and risk in markets, or whether algorithms may actually stabilise markets.The research findings will inform policy formulation and have significant impact potential. Determining the role that algorithms play in market events such as financial bubbles and 'flash crashes', where prices drop swiftly without apparent cause or news, only to bounce back shortly afterwards, will assist the finance and regulatory sector in deciding whether invention or regulatory changes are required. The experiments are also a good and inexpensive tool to test the effectiveness of new policy measures or regulations.

算法交易（Algorithmic trading）是指计算机而非人类在接收到信息的几毫秒内执行交易，多年来一直呈上升趋势。一些作者估计，美国70%的交易是由算法执行的（Swinburne 2010），其他人则高达85% （Glantz & Kissel 2013）。因此，算法交易对所有发达金融市场都产生了重大影响，但其影响尚未得到充分认识；更好地理解这种相对较新的、不断变化的现象，对于为政策辩论提供信息至关重要，比如算法交易是否需要遵守更严格的规则，以防止过度波动或资产泡沫的形成。这个项目的目的是回答关于算法交易对金融市场影响的基本问题；具体来说，通过最先进的机器学习技术训练的算法是否增加或减少了资产泡沫的可能性，它们是否增加或减少了市场的波动性和风险，以及这些算法最初是什么样的。后一个问题很简单，但不容易回答，因为成功的交易算法是有价值的，因此要保密，以免被复制、利用或监管。另一个困难是，众所周知，很难用金融市场的现场数据来确定因果关系，部分原因是，在匿名市场中，甚至无法确定哪些交易是由算法执行的。出于这些原因，我将通过实验室实验来回答这些问题，在那里我让人类交易者和算法交易者在小型实验室市场中互动。该方法的优势在于，因果关系可以令人信服地建立起来：一个只有人类交易者的对照组可以与一个有算法交易者的治疗组进行比较，根据各种结果衡量指标，如泡沫频率、波动性/风险、流动性/交易速度，其他条件相同。此外，每一个动作都可以在实验室中观察到，因此特定的交易可以被分类为由算法或人类发起的。它还允许我研究在与人类交易员或市场上的其他算法学习时，哪种算法会进化。实验室中使用的一类市场将允许受试者/算法交易多个金融资产单位。该资产在每15轮结束时向持有人支付股息。由于在以后的几轮中剩余的股息越来越少，资产的基本价值随着时间的推移而减少。然而，在之前对人类交易员的实验中，金融泡沫经常出现在价格大幅上涨的地方，尽管资产价值在下降，这种泡沫模式也在股市或楼市中观察到。因此，这种实验市场设置有助于研究泡沫的形成和泡沫的原因，但到目前为止，还没有研究算法交易在这种情况下的影响。在另一类实验市场中，我将引入金融市场交易中有关资产价值的新闻，并观察交易者和算法如何应对这种新闻冲击。特别是，主要问题是，足够数量的趋势跟踪算法是否会导致价格急剧下跌或上涨，从而增加市场的波动性和风险，或者算法是否实际上可以稳定市场。研究结果将为政策制定提供信息，并具有重大影响潜力。确定算法在金融泡沫和“闪电崩盘”等市场事件中所扮演的角色，将有助于金融和监管部门决定是否需要发明或监管变革。“闪电崩盘”是指价格在没有明显原因或新闻的情况下迅速下跌，只是在不久之后反弹。这些实验也是检验新政策措施或法规有效性的一种良好而廉价的工具。