High-frequency trading from an evolutionary perspective: Financial markets as adaptive systems

Manahov, Viktor; Hudson, Robert; Urquhart, Andrew

Abstract

The recent rapid growth of algorithmic high-frequency trading strategies makes it a very interesting time to revisit the long-standing debates about the efficiency of stock prices and the best way to model the actions of market participants. To evaluate the evolution of stock price predictability at the millisecond timeframe and to examine whether it is consistent with the newly formed adaptive market hypothesis, we develop three artificial stock markets using a strongly typed genetic programming (STGP) trading algorithm. We simulate real-life trading by applying STGP to millisecond data of the three highest capitalized stocks: Apple, Exxon Mobil, and Google and observe that profit opportunities at the millisecond time frame are better modelled through an evolutionary process involving natural selection, adaptation, learning, and dynamic evolution than by using conventional analytical techniques. We use combinations of forecasting techniques as benchmarks to demonstrate that different heuristics enable artificial traders to be ecologically rational, making adaptive decisions that combine forecasting accuracy with speed.

Citation

Manahov, V., Hudson, R., & Urquhart, A. (2018). High-frequency trading from an evolutionary perspective: Financial markets as adaptive systems. International journal of finance & economics : IJFE, 24(2), 943-962. https://doi.org/10.1002/ijfe.1700

• Journal Article Type: Article
• Acceptance Date: Sep 10, 2018
• Online Publication Date: Oct 22, 2018
• Publication Date: Oct 22, 2018
• Deposit Date: Sep 15, 2018
• Publicly Available Date: Oct 23, 2020
• Print ISSN: 1076-9307
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 24
• Issue: 2
• Pages: 943-962
• DOI: https://doi.org/10.1002/ijfe.1700
• Keywords: Adaptive market hypothesis; Efficient market hypothesis; Evolutionary computation; Genetic programming; High-frequency trading; Market efficiency
• Public URL: https://hull-repository.worktribe.com/output/1051154
• Publisher URL: https://onlinelibrary.wiley.com/doi/full/10.1002/ijfe.1700
• Contract Date: Sep 17, 2018