@article{ad7fe291475b4d1ba13981b7fb12e410,
title = "Machine learning and speed in high-frequency trading",
abstract = "The creative destruction wrought by high-frequency algorithmic trading has raised increasing concerns about the effect of machine learning behaviors and ultra high-frequency trading on financial markets. By employing a genetic algorithm with a classifier system as an adaptive learning tool, we address some of these concerns by studying a dynamic limit order market model with asymmetric information and varying speeds of high-frequency trading (HFT). We show that HFT benefits uninformed traders, improves information efficiency but reduces market liquidity. We find that there is a trade-off where a competition effect erodes the information and speed advantages of high-frequency traders, increasing trading speeds of HF traders reduces market liquidity but generates a hump-shaped relationship to the profitability of high-frequency traders and information efficiency. This research finds there may be potential benefits to throttling the trading speed arms race to improve market efficiency. We also find that strategic algorithmic trading compensates for diminishments in speed advantages, providing an insight on machine behavior in the FinTech age.",
keywords = "Genetic algorithm, High-frequency trading, Limit order market, Machine learning, Price efficiency",
author = "Jasmina Arifovic and He, \{Xue zhong\} and Lijian Wei",
note = "Funding Information: We thank the participants at the 2014 Sydney Economics and Financial Market Workshop, the 2015 and 2016 CEF conferences, and the seminars at the University of Technology Sydney, Sun Yat-Sen University, Tianjin University, and the Chinese University of Hong Kong for their valuable comments. We also thank Carl Chiarella, Shu-Heng Chen, David Easley, Giulia Iori, Blake LeBaron, Daniel Ladly, Fabrizi Lillo, Maureen O{\textquoteright}Hara, and Paolo Pellizzari for their valuable comments. Financial support from the Australian Research Council under the Discovery Grants (DP110104487, DP130103210), the National Natural Science Foundation of China Grants (71671191, U1811462, 72141304, 72171239), National Social Science Foundation of China (19ZDA103), Key Research and Development Project of Guangdong Province, China (2020B010110004) and Outstanding Youth Talent Project of Natural Science Foundation of Guangdong Provice, China (2021B1515020073) is gratefully acknowledged. Funding Information: We thank the participants at the 2014 Sydney Economics and Financial Market Workshop, the 2015 and 2016 CEF conferences, and the seminars at the University of Technology Sydney, Sun Yat-Sen University, Tianjin University, and the Chinese University of Hong Kong for their valuable comments. We also thank Carl Chiarella, Shu-Heng Chen, David Easley, Giulia Iori, Blake LeBaron, Daniel Ladly, Fabrizi Lillo, Maureen O'Hara, and Paolo Pellizzari for their valuable comments. Financial support from the Australian Research Council under the Discovery Grants (DP110104487, DP130103210), the National Natural Science Foundation of China Grants (71671191, U1811462, 72141304, 72171239), National Social Science Foundation of China (19ZDA103), Key Research and Development Project of Guangdong Province, China (2020B010110004) and Outstanding Youth Talent Project of Natural Science Foundation of Guangdong Provice, China (2021B1515020073) is gratefully acknowledged. Publisher Copyright: {\textcopyright} 2022",
year = "2022",
month = jun,
doi = "10.1016/j.jedc.2022.104438",
language = "English",
volume = "139",
journal = "Journal of Economic Dynamics and Control",
issn = "0165-1889",
}