A note on the relationship between high-frequency trading and latency arbitrage |
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| Institution: | 1. Institute of Banking and Finance, University of Graz, Universitaetsstrasse 15/F2, Graz 8010, Austria;2. Institute of Banking and Insurance, University of Applied Sciences FH Joanneum, Eggenberger Allee 11, Graz 8020, Austria;3. Finance Area, University of Mannheim, L9 1-2, Mannheim 68161, Germany;1. University of Wollongong, NSW, Australia;2. The University of Sydney Business School, Sydney, NSW, Australia;3. Finance Area, School of Business, George Mason University, , Fairfax, VA, USA, 22030;1. Faculty of Economics, Nagasaki University, Japan;2. ANU College of Business and Economics, The Australian National University, Australia;3. UWA Business School, The University of Western Australia, Australia |
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| 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. |
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