Forecasting Financial Markets Using High-Frequency Trading Data: Examination with Strongly Typed Genetic Programming. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Manahov, Viktor; Zhang, Hanxiong
• Is Part Of: International journal of electronic commerce. Volume 23:Issue 1(2019)
• Page Start: 12
• Page End: 32
• Publication Date: 2019-01-02
• Subjects: Evolutionary computation -- artificial intelligence -- high-frequency trading -- algorithmic trading -- big data analytics -- financial econometrics; Electronic commerce -- Periodicals; Electronic commerce; Periodicals; Electronic journals; 381.14205
• DOI: 10.1080/10864415.2018.1512271
• Languages: English
• ISSNs: 1086-4415

Abstract

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.