Experiments on the effects of trading algorithms on financial markets

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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.