Manahov, V. and Zhang, H. orcid.org/0000-0002-8727-4906 (2019) Forecasting financial markets using high-frequency trading data: Examination with strongly typed genetic programming. International Journal of Electronic Commerce, 23 (1). pp. 12-32. ISSN 1086-4415
Abstract
Market regulators around the world are still debating whether high-frequency trading (HFT) plays a positive or negative role in market quality. We develop an artificial futures market populated with high-frequency traders (HFTs) and institutional traders using Strongly Typed Genetic Programming (STGP) trading algorithm. We simulate real-life futures trading at the millisecond time frame by applying STGP to E-Mini S&P 500 data stamped at the millisecond interval. A direct forecasting comparison between HFTs and institutional traders indicate the superiority of the former. We observe that the negative implications of high-frequency trading in futures markets can be mitigated by introducing a minimum resting trading period of less than 50 milliseconds. Overall, we contribute to the e-commerce literature by showing that minimum resting trading order period of less than 50 milliseconds could lead to HFTs facing a queuing risk resulting in a less harmful market quality effect. One practical implication of our study is that we demonstrate that market regulators and/or e-commerce practitioners can apply artificial intelligence tools such as STGP to conduct trading behavior-based profiling. This can be used to detect the occurrence of new HFT strategies and examine their impact on the futures market.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Taylor & Francis Group, LLC. This is an author produced version of a paper subsequently published in International Journal of Electronic Commerce. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Evolutionary computation; artificial intelligence; high-frequency trading; algorithmic trading; big data analytics; financial econometrics |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Management School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Mar 2019 15:00 |
Last Modified: | 06 Jul 2020 00:39 |
Status: | Published |
Publisher: | Taylor & Francis |
Refereed: | Yes |
Identification Number: | 10.1080/10864415.2018.1512271 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:143979 |