Enhancing Trading Strategies with Order Book Signals

38 Pages Posted: 3 Oct 2015 Last revised: 14 Oct 2015

See all articles by Álvaro Cartea

Álvaro Cartea

University of Oxford; University of Oxford - Oxford-Man Institute of Quantitative Finance

Ryan Francis Donnelly

University of Washington - Department of Applied Mathematics

Sebastian Jaimungal

University of Toronto - Department of Statistics

Date Written: October 1, 2015

Abstract

We use high-frequency data from the Nasdaq exchange to build a measure of volume imbalance in the limit order book (LOB). We show that our measure is a good predictor of the sign of the next market order (MO), i.e. buy or sell, and also helps to predict price changes immediately after the arrival of an MO. Based on these empirical findings, we introduce and calibrate a Markov chain modulated pure jump model of price, spread, LO and MO arrivals, and volume imbalance. As an application of the model, we pose and solve a stochastic control problem for an agent who maximizes terminal wealth, subject to inventory penalties, by executing trades using LOs. We use in-sample-data (January to June 2014) to calibrate the model to ten equities traded in the Nasdaq exchange, and use out-of-sample data (July to December 2014) to test the performance of the strategy. We show that introducing our volume imbalance measure into the optimization problem considerably boosts the profits of the strategy. Profits increase because employing our imbalance measure reduces adverse selection costs and positions LOs in the book to take advantage of favorable price movements.

Keywords: order imbalance, algorithmic trading, high-frequency trading, order flow, market making, adverse selection

JEL Classification: G10, G11, G14, C41

Suggested Citation

Cartea, Álvaro and Donnelly, Ryan Francis and Jaimungal, Sebastian, Enhancing Trading Strategies with Order Book Signals (October 1, 2015). Available at SSRN: https://ssrn.com/abstract=2668277 or http://dx.doi.org/10.2139/ssrn.2668277

Álvaro Cartea

University of Oxford ( email )

Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

Ryan Francis Donnelly

University of Washington - Department of Applied Mathematics ( email )

Box 352420
Seattle, WA 98195-2420
United States

Sebastian Jaimungal (Contact Author)

University of Toronto - Department of Statistics ( email )

100 St. George St.
Toronto, Ontario M5S 3G3
Canada

HOME PAGE: http://www.utstat.utoronto.ca/sjaimung

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