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Order Flows and Limit Order Book Resiliency on the Meso-Scale

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  • Kyle Bechler
  • Michael Ludkovski

Abstract

We investigate the behavior of limit order books on the meso-scale motivated by order execution scheduling algorithms. To do so we carry out empirical analysis of the order flows from market and limit order submissions, aggregated from tick-by-tick data via volume-based bucketing, as well as various LOB depth and shape metrics. We document a nonlinear relationship between trade imbalance and price change, which however can be converted into a linear link by considering a weighted average of market and limit order flows. We also document a hockey-stick dependence between trade imbalance and one-sided limit order flows, highlighting numerous asymmetric effects between the active and passive sides of the LOB. To address the phenomenological features of price formation, book resilience, and scarce liquidity we apply a variety of statistical models to test for predictive power of different predictors. We show that on the meso-scale the limit order flows (as well as the relative addition/cancellation rates) carry the most predictive power. Another finding is that the deeper LOB shape, rather than just the book imbalance, is more relevant on this timescale. The empirical results are based on analysis of six large-tick assets from Nasdaq.

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  • Kyle Bechler & Michael Ludkovski, 2017. "Order Flows and Limit Order Book Resiliency on the Meso-Scale," Papers 1708.02715, arXiv.org.
    • Handle: RePEc:arx:papers:1708.02715
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    References listed on IDEAS

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    Cited by:

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    2. Miko{l}aj Bi'nkowski & Charles-Albert Lehalle, 2018. "Endogeneous Dynamics of Intraday Liquidity," Papers 1811.03766, arXiv.org.
    3. Xuan Tao & Andrew Day & Lan Ling & Samuel Drapeau, 2020. "On Detecting Spoofing Strategies in High Frequency Trading," Papers 2009.14818, arXiv.org, revised Dec 2020.
    4. Eyal Neuman & Moritz Vo{ss}, 2020. "Optimal Signal-Adaptive Trading with Temporary and Transient Price Impact," Papers 2002.09549, arXiv.org, revised Jan 2022.

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