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

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

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.

adaptive market hypothesis, efficient market hypothesis, evolutionary computation, genetic programming, high-frequency trading, market efficiency
1076-9307
1-20
Manahov, Viktor
6df07476-f354-4c89-92ad-9364dd096ee9
Hudson, Robert
b6acb793-0e18-4ba8-a4c5-bd671e29e5f7
Urquhart, Andrew
ee369df1-95b5-4cdf-bc24-f1be77357c03
Manahov, Viktor
6df07476-f354-4c89-92ad-9364dd096ee9
Hudson, Robert
b6acb793-0e18-4ba8-a4c5-bd671e29e5f7
Urquhart, Andrew
ee369df1-95b5-4cdf-bc24-f1be77357c03

Manahov, Viktor, Hudson, Robert and Urquhart, Andrew (2018) High-frequency trading from an evolutionary perspective: financial markets as adaptive systems. International Journal of Finance and Economics, 1-20. (doi:10.1002/ijfe.1700).

Record type: Article

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.

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Accepted/In Press date: 10 September 2018
e-pub ahead of print date: 22 October 2018
Keywords: adaptive market hypothesis, efficient market hypothesis, evolutionary computation, genetic programming, high-frequency trading, market efficiency

Identifiers

Local EPrints ID: 426151
URI: http://eprints.soton.ac.uk/id/eprint/426151
ISSN: 1076-9307
PURE UUID: b6fba8e3-8410-4bd3-995e-1b1b593f681e
ORCID for Andrew Urquhart: ORCID iD orcid.org/0000-0001-8834-4243

Catalogue record

Date deposited: 15 Nov 2018 17:30
Last modified: 28 Apr 2022 04:59

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Contributors

Author: Viktor Manahov
Author: Robert Hudson
Author: Andrew Urquhart ORCID iD

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