Queue Imbalance as a One-Tick-Ahead Price Predictor in a Limit Order Book

30 Pages Posted: 12 Dec 2015

See all articles by Martin Gould

Martin Gould

Imperial College London - Department of Mathematics

Julius Bonart

Imperial College London, CFM-Imperial Institute of Quantitative Finance; University College London - Financial Computing and Analytics Group, Department of Computer Science

Date Written: December 11, 2015

Abstract

We investigate whether the bid/ask queue imbalance in a limit order book (LOB) provides significant predictive power for the direction of the next mid-price movement. We consider this question both in the context of a simple binary classifier, which seeks to predict the direction of the next mid-price movement, and a probabilistic classifier, which seeks to predict the probability that the next mid-price movement will be upwards. To implement these classifiers, we fit logistic regressions between the queue imbalance and the direction of the subsequent mid-price movement for each of 10 liquid stocks on Nasdaq. In each case, we find a strongly statistically significant relationship between these variables. Compared to a simple null model, which assumes that the direction of mid-price changes is uncorrelated with the queue imbalance, we find that our logistic regression fits provide a considerable improvement in binary and probabilistic classification for large-tick stocks, and provide a moderate improvement in binary and probabilistic classification for small-tick stocks. We also perform local logistic regression fits on the same data, and find that this semi-parametric approach slightly outperform our logistic regression fits, at the expense of being more computationally intensive to implement.

Keywords: Price prediction, queue imbalance, high-frequency trading, limit order books, market microstructure

Suggested Citation

Gould, Martin and Bonart, Julius, Queue Imbalance as a One-Tick-Ahead Price Predictor in a Limit Order Book (December 11, 2015). Available at SSRN: https://ssrn.com/abstract=2702117 or http://dx.doi.org/10.2139/ssrn.2702117

Martin Gould (Contact Author)

Imperial College London - Department of Mathematics ( email )

South Kensington Campus
Imperial College
London, SW7 2AZ
United Kingdom

HOME PAGE: http://www.imperial.ac.uk/people/m.gould

Julius Bonart

Imperial College London, CFM-Imperial Institute of Quantitative Finance ( email )

South Kensington Campus
Imperial College
LONDON, SW7 2AZ
United Kingdom

University College London - Financial Computing and Analytics Group, Department of Computer Science ( email )

Gower Street
London, WC1E 6BT
United Kingdom

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