Forecasting prices from level-I quotes in the presence of hidden liquidity

Marco Avellaneda; Josh Reed; Sasha Stoikov

doi:10.3233/AF-2011-004

Forecasting prices from level-I quotes in the presence of hidden liquidity

Marco Avellaneda, Josh Reed, Sasha Stoikov

Mathematics

Research output: Contribution to journal › Article › peer-review

Abstract

Bid and ask sizes at the top of the order book provide information on short-term price moves. Drawing from classical descriptions of the order book in terms of queues and order-arrival rates (Smith et al., 2003), we consider a diffusion model for the evolution of the best bid/ask queues. We compute the probability that the next price move is upward, conditional on the best bid/ask sizes, the hidden liquidity in the market and the correlation between changes in the bid/ask sizes. The model can be useful, among other things, to rank trading venues in terms of the 'information content' of their quotes and to estimate hidden liquidity in a market based on high-frequency data. We illustrate the approach with an empirical study of a few stocks using quotes from various exchanges.

Original language	English (US)
Pages (from-to)	35-43
Number of pages	9
Journal	Algorithmic Finance
Volume	1
Issue number	1
DOIs	https://doi.org/10.3233/AF-2011-004
State	Published - 2011

ASJC Scopus subject areas

Finance
Computer Vision and Pattern Recognition
Computer Science Applications
Computational Mathematics

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abstract = "Bid and ask sizes at the top of the order book provide information on short-term price moves. Drawing from classical descriptions of the order book in terms of queues and order-arrival rates (Smith et al., 2003), we consider a diffusion model for the evolution of the best bid/ask queues. We compute the probability that the next price move is upward, conditional on the best bid/ask sizes, the hidden liquidity in the market and the correlation between changes in the bid/ask sizes. The model can be useful, among other things, to rank trading venues in terms of the 'information content' of their quotes and to estimate hidden liquidity in a market based on high-frequency data. We illustrate the approach with an empirical study of a few stocks using quotes from various exchanges.",

author = "Marco Avellaneda and Josh Reed and Sasha Stoikov",

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Forecasting prices from level-I quotes in the presence of hidden liquidity

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