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.
| Original language | English |
|---|---|
| Pages (from-to) | 281-296 |
| Number of pages | 16 |
| Journal | International Review of Financial Analysis |
| Volume | 47 |
| Early online date | 5 Jul 2016 |
| DOIs | |
| Publication status | Published - Oct 2016 |
Bibliographical note
© 2016 Published by Elsevier Inc.This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.Keywords
- Agent-based modelling
- Algorithmic trading
- Genetic programming
- High frequency trading
- Market efficiency
- Market regulation
