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Investigating Limit Order Book Characteristics for Short Term Price Prediction: a Machine Learning Approach

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  • Faisal I Qureshi

Abstract

With the proliferation of algorithmic high-frequency trading in financial markets, the Limit Order Book has generated increased research interest. Research is still at an early stage and there is much we do not understand about the dynamics of Limit Order Books. In this paper, we employ a machine learning approach to investigate Limit Order Book features and their potential to predict short term price movements. This is an initial broad-based investigation that results in some novel observations about LOB dynamics and identifies several promising directions for further research. Furthermore, we obtain prediction results that are significantly superior to a baseline predictor.

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  • Faisal I Qureshi, 2018. "Investigating Limit Order Book Characteristics for Short Term Price Prediction: a Machine Learning Approach," Papers 1901.10534, arXiv.org.
    • Handle: RePEc:arx:papers:1901.10534
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    References listed on IDEAS

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    1. 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.
    2. Reginald D. Smith, 2010. "Is high-frequency trading inducing changes in market microstructure and dynamics?," Papers 1006.5490, arXiv.org, revised Sep 2010.
    3. Julius Bonart & Martin D. Gould, 2017. "Latency and liquidity provision in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 17(10), pages 1601-1616, October.
    4. Ban Zheng & Eric Moulines & Fr'ed'eric Abergel, 2012. "Price Jump Prediction in Limit Order Book," Papers 1204.1381, arXiv.org.
    5. Tzu-Wei Yang & Lingjiong Zhu, 2015. "A reduced-form model for level-1 limit order books," Papers 1508.07891, arXiv.org, revised Nov 2016.
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