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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. 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).
    2. 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.
    3. Ye, Wuyi & Yang, Jinting & Chen, Pengzhan, 2024. "Short-term stock price trend prediction with imaging high frequency limit order book data," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1189-1205.
    4. 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.
    5. Long, Yunshen & Yan, Jingzhou & Wu, Liang & Long, Xingchen, 2024. "Market price determination: Interpreting quote order imbalance under zero-profit equilibrium," Economic Modelling, Elsevier, vol. 134(C).
    6. Rama Cont & Adrien De Larrard, 2012. "Order book dynamics in liquid markets: limit theorems and diffusion approximations," Papers 1202.6412, arXiv.org.
    7. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books," Papers 1808.03668, arXiv.org, revised Jan 2020.
    8. Baron Law & Frederi Viens, 2019. "Market Making under a Weakly Consistent Limit Order Book Model," Papers 1903.07222, arXiv.org, revised Jan 2020.
    9. 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.
    10. Justin Sirignano, 2016. "Deep Learning for Limit Order Books," Papers 1601.01987, arXiv.org, revised Jul 2016.
    11. Rama Cont & Adrien de Larrard, 2011. "Order book dynamics in liquid markets: limit theorems and diffusion approximations," Working Papers hal-00672274, HAL.
    12. 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.
    13. Parley Ruogu Yang, 2021. "Forecasting high-frequency financial time series: an adaptive learning approach with the order book data," Papers 2103.00264, arXiv.org.
    14. Bastien Baldacci & Iuliia Manziuk, 2020. "Adaptive trading strategies across liquidity pools," Papers 2008.07807, arXiv.org.
    15. Olivier Guéant, 2016. "The Financial Mathematics of Market Liquidity: From Optimal Execution to Market Making," Post-Print hal-01393136, HAL.
    16. 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.
    17. Tzu-Wei Yang & Lingjiong Zhu, 2015. "A reduced-form model for level-1 limit order books," Papers 1508.07891, arXiv.org, revised Nov 2016.
    18. 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.

    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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