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CME Iceberg Order Detection and Prediction

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  • Dmitry Zotikov
  • Anton Antonov

Abstract

We propose a method for detection and prediction of native and synthetic iceberg orders on Chicago Mercantile Exchange. Native (managed by the exchange) icebergs are detected using discrepancies between the resting volume of an order and the actual trade size as indicated by trade summary messages, as well as by tracking order modifications that follow trade events. Synthetic (managed by market participants) icebergs are detected by observing limit orders arriving within a short time frame after a trade. The obtained icebergs are then used to train a model based on the Kaplan--Meier estimator, accounting for orders that were cancelled after a partial execution. The model is utilized to predict the total size of newly detected icebergs. Out of sample validation is performed on the full order depth data, performance metrics and quantitative estimates of hidden volume are presented.

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  • Dmitry Zotikov & Anton Antonov, 2019. "CME Iceberg Order Detection and Prediction," Papers 1909.09495, arXiv.org.
    • Handle: RePEc:arx:papers:1909.09495
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    References listed on IDEAS

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    1. Fleming, Michael J. & Mizrach, Bruce & Nguyen, Giang, 2018. "The microstructure of a U.S. Treasury ECN: The BrokerTec platform," Journal of Financial Markets, Elsevier, vol. 40(C), pages 2-22.
    2. Stefan Frey & Patrik Sandås, 2017. "The Impact of Iceberg Orders in Limit Order Books," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 7(03), pages 1-43, September.
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