A note on the relationship between high-frequency trading and latency arbitrage

Viktor Manahov

International Review of Financial Analysis, 2016, vol. 47, issue C, 281-296

Abstract: We develop three artificial stock markets populated with two types of market participants — HFT scalpers and aggressive high frequency traders (HFTrs). We simulate real-life trading at the millisecond interval by applying Strongly Typed Genetic Programming (STGP) to real-time data from Cisco Systems, Intel and Microsoft. We observe that HFT scalpers are able to calculate NASDAQ NBBO (National Best Bid and Offer) at least 1.5ms ahead of the NASDAQ SIP (Security Information Processor), resulting in a large number of latency arbitrage opportunities. We also demonstrate that market efficiency is negatively affected by the latency arbitrage activity of HFT scalpers, with no countervailing benefit in volatility or any other measured variable. To improve market quality, and eliminate the socially wasteful arms race for speed, we propose batch auctions in every 70ms of trading.

Keywords: Agent-based modelling; High frequency trading; Algorithmic trading; Market regulation; Market efficiency; Genetic programming (search for similar items in EconPapers)
JEL-codes: G10 G12 G14 G19 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:47:y:2016:i:c:p:281-296

DOI: 10.1016/j.irfa.2016.06.014

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