Forecasting Financial Markets Using High-Frequency Trading Data: Examination with Strongly Typed Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{Manahov:2019:ijec,

• author = "Viktor Manahov and Hanxiong Zhang",
• title = "Forecasting Financial Markets Using High-Frequency Trading Data: Examination with Strongly Typed Genetic Programming",
• journal = "International Journal of Electronic Commerce",
• year = "2019",
• volume = "23",
• number = "1",
• pages = "12--32",
• keywords = "genetic algorithms, genetic programming, STGP, evolutionary computation, artificial intelligence, high-frequency trading, algorithmic trading, big data analytics, financial econometrics",
• publisher = "Routledge",
• ISSN = "1086-4415",
• bibsource = "OAI-PMH server at eprints.lincoln.ac.uk",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijecommerce/ijecommerce23.html#ManahovZ19",
• language = "en",
• oai = "oai:eprints.lincoln.ac.uk:32097",
• URL = "http://eprints.lincoln.ac.uk/32097/",
• URL = "http://eprints.lincoln.ac.uk/32097/1/Forecasting%20Financial%20Markets%20Using%20HighFrequency%20Trading%20Data%20Examination%20with%20Strongly%20Typed%20Genetic%20Programming.docx",
• DOI = "doi:10.1080/10864415.2018.1512271",
• abstract = "Market regulators around the world are still debating whether or not 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 timeframe 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 behaviour-based profiling. This can be used to detect the occurrence of new HFT strategies and examine their impact on the futures market.",
• notes = "Also known as \cite{journals/ijecommerce/ManahovZ19}",

}

Genetic Programming entries for Viktor Manahov Hanxiong Zhang

Citations