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

Author:

Avellaneda Marco1,Reed Josh2,Stoikov Sasha3

Affiliation:

1. Courant Institute, New York University and Finance Concepts LLC, NY, USA

2. Stern School of Business, New York University, NY, USA

3. Cornell Financial Engineering Manhattan, NY, USA. Email: sfs33@cornell.edu

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.

Publisher

IOS Press

Subject

Computational Mathematics,Computer Science Applications,Computer Vision and Pattern Recognition,Finance

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