Algorithmic Trading, Stochastic Control, and Mutually Exciting Processes
Author(s) -
Álvaro Cartea,
Sebastian Jaimungal,
Jason Ricci
Publication year - 2018
Publication title -
siam review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.683
H-Index - 120
eISSN - 1095-7200
pISSN - 0036-1445
DOI - 10.1137/18m1176968
Subject(s) - high frequency trading , algorithmic trading , trading strategy , limit (mathematics) , pairs trade , term (time) , order (exchange) , profit (economics) , cluster analysis , order book , process (computing) , computer science , financial market , control (management) , economics , financial economics , econometrics , microeconomics , finance , mathematics , alternative trading system , artificial intelligence , physics , mathematical analysis , quantum mechanics , operating system
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 multifactor mutually 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 the 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 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.
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