The high frequency trade off between speed and sophistication

Abstract

Central to the ability of a high frequency trader to make money is speed. In order to be first to trading opportunities, firms invest in the fastest hardware and the shortest connections between their machines and the markets. Yet this is not enough: algorithms must be short, no more than a few instructions. As a result there is a trade-off in the design of optimal high frequency trading strategies: being the fastest necessitates being less sophisticated. To understand the effect of this tension a computational model is presented that captures latency, both of code execution and information transmission. Trading algorithms are modelled through genetic programming with longer programmes allowing more sophisticated decisions at the cost of slower execution times. It is shown that, depending on the market composition, short fast strategies and slower more sophisticated strategies may both be viable and exploit different trading opportunities. The relative profits of these different approaches vary, however, slow traders benefit and social welfare increase in the presence of HFTs. A suite of regulations are tested to manage the risks associated with high frequency trading, the majority are found to be ineffective, though constraining the ratio of orders to trades may be promising.