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Using full limit order book for price jump prediction


  • Mynbaev, Kairat


Institutional investors, especially high frequency traders, employ the order information contained in the Limit Order Book (LOB). The main purpose of the paper is to investigate how full information about the LOB can help in predicting the price jump. Normally, a full LOB contains total volumes of orders for hundreds of prices. Using the full information runs into the curse of dimensionality which manifests itself in multicollinearity, insignificant coefficients, inflated estimate variances and high computation time. Due to these problems, order volumes for prices that are distant from ask and bid prices are usually not used in prediction procedures. For this reason we call such information a silent crowd. Here we propose a summary measure of the silent crowd and quantify its influence on trade jump prediction. We use a realistically simulated LOB as a vehicle for experiments and logistic regression as the prediction tool. The full code in Matlab includes 18 blocks.

Suggested Citation

  • Mynbaev, Kairat, 2020. "Using full limit order book for price jump prediction," MPRA Paper 101684, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:101684

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    References listed on IDEAS

    1. Thierry Foucault & Ohad Kadan & Eugene Kandel, 2005. "Limit Order Book as a Market for Liquidity," Review of Financial Studies, Society for Financial Studies, vol. 18(4), pages 1171-1217.
    2. Rama Cont & Sasha Stoikov & Rishi Talreja, 2010. "A Stochastic Model for Order Book Dynamics," Operations Research, INFORMS, vol. 58(3), pages 549-563, June.
    3. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2019. "Price Discovery without Trading: Evidence from Limit Orders," Journal of Finance, American Finance Association, vol. 74(4), pages 1621-1658, August.
    4. Jean-Philippe Bouchaud & Marc Mezard & Marc Potters, 2002. "Statistical properties of stock order books: empirical results and models," Science & Finance (CFM) working paper archive 0203511, Science & Finance, Capital Fund Management.
    5. He Huang & Alec N. Kercheval, 2012. "A generalized birth--death stochastic model for high-frequency order book dynamics," Quantitative Finance, Taylor & Francis Journals, vol. 12(4), pages 547-557, August.
    6. Ioanid Rosu, 2009. "A Dynamic Model of the Limit Order Book," Post-Print hal-00515873, HAL.
    7. Alec N. Kercheval & Yuan Zhang, 2015. "Modelling high-frequency limit order book dynamics with support vector machines," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1315-1329, August.
    8. Tristan Fletcher & John Shawe-Taylor, 2013. "Multiple Kernel Learning with Fisher Kernels for High Frequency Currency Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 42(2), pages 217-240, August.
    9. Jean-Philippe Bouchaud & Marc Mezard & Marc Potters, 2002. "Statistical properties of stock order books: empirical results and models," Quantitative Finance, Taylor & Francis Journals, vol. 2(4), pages 251-256.
    10. Federico Platania & Pedro Serrano & Mikel Tapia, 2018. "Modelling the shape of the limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 18(9), pages 1575-1597, September.
    11. Ioanid Rosu, 2009. "A Dynamic Model of the Limit Order Book," Review of Financial Studies, Society for Financial Studies, vol. 22(11), pages 4601-4641, November.
    12. Ban Zheng & Eric Moulines & Frédéric Abergel, 2013. "Price jump prediction in a limit order book," Post-Print hal-00684716, HAL.
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    More about this item


    Simulation; trade jump prediction; high frequency trading; logistic regression; limit order book;

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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