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

Author

Listed:
  • Avellaneda, Marco

    (Courant Institute, New York University and Finance Concepts LLC, NY, USA)

  • Reed, Josh

    (Stern School of Business, New York University, NY, USA)

  • Stoikov, Sasha

    (Cornell Financial Engineering Manhattan, NY, USA)

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

Suggested Citation

  • Avellaneda, Marco & Reed, Josh & Stoikov, Sasha, 2011. "Forecasting prices from level-I quotes in the presence of hidden liquidity," Algorithmic Finance, IOS Press, vol. 1(1), pages 35-43.
    • Handle: RePEc:ris:iosalg:0004
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    Citations

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    Cited by:

    1. Ben Hambly & Jasdeep Kalsi & James Newbury, 2018. "Limit order books, diffusion approximations and reflected SPDEs: from microscopic to macroscopic models," Papers 1808.07107, arXiv.org, revised Jun 2019.
    2. Helder Rojas & Artem Logachov & Anatoly Yambartsev, 2023. "Order Book Dynamics with Liquidity Fluctuations: Asymptotic Analysis of Highly Competitive Regime," Mathematics, MDPI, vol. 11(20), pages 1-24, October.
    3. Rama Cont & Adrien De Larrard, 2012. "Order book dynamics in liquid markets: limit theorems and diffusion approximations," Papers 1202.6412, arXiv.org.
    4. Rama Cont & Adrien de Larrard, 2011. "Order book dynamics in liquid markets: limit theorems and diffusion approximations," Working Papers hal-00672274, HAL.
    5. Martin D. Gould & Julius Bonart, 2015. "Queue Imbalance as a One-Tick-Ahead Price Predictor in a Limit Order Book," Papers 1512.03492, arXiv.org.
    6. Bastien Baldacci & Iuliia Manziuk, 2020. "Adaptive trading strategies across liquidity pools," Papers 2008.07807, arXiv.org.
    7. Olivier Guéant, 2016. "The Financial Mathematics of Market Liquidity: From Optimal Execution to Market Making," Post-Print hal-01393136, HAL.
    8. Jin Fang & Jiacheng Weng & Yi Xiang & Xinwen Zhang, 2022. "Imitate then Transcend: Multi-Agent Optimal Execution with Dual-Window Denoise PPO," Papers 2206.10736, arXiv.org.
    9. Tzu-Wei Yang & Lingjiong Zhu, 2015. "A reduced-form model for level-1 limit order books," Papers 1508.07891, arXiv.org, revised Nov 2016.
    10. A. Gareche & G. Disdier & J. Kockelkoren & J. -P. Bouchaud, 2013. "A Fokker-Planck description for the queue dynamics of large tick stocks," Papers 1304.6819, arXiv.org.
    11. Sim, Min Kyu & Deng, Shijie, 2020. "Estimation of level-I hidden liquidity using the dynamics of limit order-book," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    12. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books," Papers 1808.03668, arXiv.org, revised Jan 2020.
    13. Baron Law & Frederi Viens, 2019. "Market Making under a Weakly Consistent Limit Order Book Model," Papers 1903.07222, arXiv.org, revised Jan 2020.
    14. Helder Rojas & Artem Logachov & Anatoly Yambartsev, 2020. "Order book dynamics with liquidity fluctuations: limit theorems and large deviations," Papers 2004.10632, arXiv.org, revised Jan 2021.
    15. Justin Sirignano, 2016. "Deep Learning for Limit Order Books," Papers 1601.01987, arXiv.org, revised Jul 2016.
    16. Parley Ruogu Yang, 2021. "Forecasting high-frequency financial time series: an adaptive learning approach with the order book data," Papers 2103.00264, arXiv.org.

    More about this item

    JEL classification:

    • A10 - General Economics and Teaching - - General Economics - - - General
    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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