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Buy Low Sell High: A High Frequency Trading Perspective


Álvaro Cartea


University College London

Sebastian Jaimungal


University of Toronto - Department of Statistics

Jason Ricci


University of Toronto, Department of Statistics

November 25, 2011


Abstract:     
We develop a High Frequency (HF) trading strategy where the HF trader uses her superior speed to process information and to post limit sell and buy orders. By introducing a multi-factor self-exciting process we allow for feedback effects in market buy and sell orders and the shape of the limit order book (LOB). Our model accounts for arrival of market orders that influence activity, trigger one-sided and two-sided clustering of trades, and induce temporary changes in the shape of the LOB. We also model the impact that market orders and news have on the short-term drift of the midprice (short-term-alpha). We show that HF traders who do not include predictors of short-term-alpha in their strategies are driven out of the market because they are adversely selected by better informed traders and because they are not able to profit from directional strategies.

Number of Pages in PDF File: 41

Keywords: Algorithmic Trading, High Frequency Trading, Short Term Alpha, Adverse Selection, Self-Exciting Processes, Hawkes processes

JEL Classification: C6, C61, C63, G1, G10, G12, G17, G60

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Date posted: November 26, 2011 ; Last revised: December 31, 2012

Suggested Citation

Cartea, Álvaro, Jaimungal, Sebastian and Ricci, Jason, Buy Low Sell High: A High Frequency Trading Perspective (November 25, 2011). Available at SSRN: http://ssrn.com/abstract=1964781 or http://dx.doi.org/10.2139/ssrn.1964781

Contact Information

Álvaro Cartea (Contact Author)
University College London ( email )
Gower Street
London, WC1E 6BT
United Kingdom
HOME PAGE: http://www.cartea.net
Sebastian Jaimungal
University of Toronto - Department of Statistics ( email )
Toronto, Ontario M5S 3G3
Canada
HOME PAGE: http://www.utstat.utoronto.ca/sjaimung
Jason Ricci
University of Toronto, Department of Statistics ( email )
Toronto, Ontario
Canada
Feedback to SSRN (Beta)


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