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

Manahov, Viktor and Zhang, Hanxiong (2018) Forecasting Financial Markets Using High-Frequency Trading Data: Examination with Strongly Typed Genetic Programming. International Journal of Electronic Commerce . ISSN 1086-4415

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

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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.

Keywords:Evolutionary Computation, Artificial Intelligence, High-Frequency Trading, Algorithmic Trading, Big Data Analytics, Financial Econometrics
Subjects:N Business and Administrative studies > N341 Financial Risk
N Business and Administrative studies > N990 Business and Administrative studies not elsewhere classified
N Business and Administrative studies > N321 Investment
N Business and Administrative studies > N300 Finance
Divisions:Lincoln International Business School
ID Code:32097
Deposited On:27 Jun 2018 22:10

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