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

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@Article{Manahov:2016:IRFA,

• author = "Viktor Manahov",
• title = "A note on the relationship between high-frequency trading and latency arbitrage",
• journal = "International Review of Financial Analysis",
• year = "2016",
• volume = "47",
• pages = "281--296",
• keywords = "genetic algorithms, genetic programming, Agent-based modelling, High frequency trading, Algorithmic trading, Market regulation, Market efficiency",
• ISSN = "1057-5219",
• URL = "http://www.sciencedirect.com/science/article/pii/S1057521916301090",
• URL = "https://eprints.whiterose.ac.uk/117919/1/A_note_on_the_relationship_between_high_frequency_trading.pdf",
• DOI = "doi:10.1016/j.irfa.2016.06.014",
• 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 1.5 ms 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 70 ms of trading.",

}

Genetic Programming entries for Viktor Manahov

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