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Price jump prediction in a limit order book

Author

Listed:
  • Ban Zheng

    (LTCI - Laboratoire Traitement et Communication de l'Information - Télécom ParisTech - IMT - Institut Mines-Télécom [Paris] - CNRS - Centre National de la Recherche Scientifique, FiQuant - Chaire de finance quantitative - MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec)

  • Eric Moulines

    (LTCI - Laboratoire Traitement et Communication de l'Information - Télécom ParisTech - IMT - Institut Mines-Télécom [Paris] - CNRS - Centre National de la Recherche Scientifique)

  • Frédéric Abergel

    (FiQuant - Chaire de finance quantitative - MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec, MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris)

Abstract

A limit order book provides information on available limit order prices and their volumes. Based on these quantities, we give an empirical result on the relationship between the bid-ask liquidity balance and trade sign and we show that liquidity balance on best bid/best ask is quite informative for predicting the future market order's direction. Moreover, we de ne price jump as a sell (buy) market order arrival which is executed at a price which is smaller (larger) than the best bid (best ask) price at the moment just after the precedent market order arrival. Features are then extracted related to limit order volumes, limit order price gaps, market order information and limit order event information. Logistic regression is applied to predict the price jump from the limit order book's feature. LASSO logistic regression is introduced to help us make variable selection from which we are capable to highlight the importance of di erent features in predicting the future price jump. In order to get rid of the intraday data seasonality, the analysis is based on two separated datasets: morning dataset and afternoon dataset. Based on an analysis on forty largest French stocks of CAC40, we nd that trade sign and market order size as well as the liquidity on the best bid (best ask) are consistently informative for predicting the incoming price jump.

Suggested Citation

  • Ban Zheng & Eric Moulines & Frédéric Abergel, 2013. "Price jump prediction in a limit order book," Post-Print hal-00684716, HAL.
    • Handle: RePEc:hal:journl:hal-00684716
    DOI: 10.4236/jmf.2013.3202
    Note: View the original document on HAL open archive server: https://hal.science/hal-00684716v2
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    References listed on IDEAS

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    Cited by:

    1. Söhnke M. Bartram & Jürgen Branke & Mehrshad Motahari, 2020. "Artificial intelligence in asset management," Working Papers 20202001, Cambridge Judge Business School, University of Cambridge.
    2. Paweł Mielcarz & Dmytro Osiichuk & Jarosław Cymerski, 2020. "Algorithmic Sangfroid? The Decline of Sensitivity of Crude Oil Prices to News on Potentially Disruptive Terror Attacks and Political Unrest," Sustainability, MDPI, vol. 13(1), pages 1-24, December.
    3. Matthew F Dixon, 2017. "Sequence Classification of the Limit Order Book using Recurrent Neural Networks," Papers 1707.05642, arXiv.org.
    4. Ao Kong & Hongliang Zhu & Robert Azencott, 2021. "Predicting intraday jumps in stock prices using liquidity measures and technical indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 416-438, April.
    5. Ao Kong & Hongliang Zhu & Robert Azencott, 2019. "Predicting intraday jumps in stock prices using liquidity measures and technical indicators," Papers 1912.07165, arXiv.org.
    6. Matthew F Dixon, 2017. "A High Frequency Trade Execution Model for Supervised Learning," Papers 1710.03870, arXiv.org, revised Dec 2017.
    7. Mynbaev, Kairat, 2020. "Using full limit order book for price jump prediction," MPRA Paper 101684, University Library of Munich, Germany.

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    Keywords

    limit order book; price jumps; predictibility; LASSO;
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