CME Iceberg Order Detection and Prediction

TL;DR

This paper introduces a novel method for detecting and predicting iceberg orders on the CME, combining order discrepancy analysis and a Kaplan--Meier based model to estimate hidden volumes and forecast iceberg sizes.

Contribution

It presents a new approach for identifying native and synthetic icebergs and employs a Kaplan--Meier estimator for predicting iceberg sizes, enhancing market transparency tools.

Findings

Effective detection of native and synthetic icebergs.

Accurate prediction of iceberg total sizes.

Quantitative estimates of hidden volumes.

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.