CME iceberg order detection and prediction

We propose a method for detection and prediction of native and synthetic iceberg orders on the Chicago Mercantile Exchange. Native (managed by the exchange) iceberg orders 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 the tracking order modifications that follow trade events. Synthetic (managed by market participants) iceberg orders are detected by observing limit orders arriving within a short time frame after a trade. The obtained iceberg orders 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 utilised to predict the total size of newly detected iceberg orders. Out of sample validation is performed on the full order depth data, performance metrics and quantitative estimates of hidden volume are presented.