CEBA talks 2020-2021
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A Machine Learning Attack on Predatory Trading

Authors: Robert James, Henry Leung and Artem Prokhorov


Abstract: We design an adaptive framework for detection of predatory trading behavior. Its key component is an extension of a pattern recognition tool, originating from the fields of engineering and signal processing and adapted to modern electronic systems of securities trading. The new methodology combines flexibility of dynamic time warping, rigor of extreme value theory and richness of order book data of an exchange to accurately identify predatory trading without access to many confirmed illegal transactions for training. The method is shown to achieve significant improvements over alternative approaches in the identification of illegal insider trading cases included in a high-frequency dataset provided by an large investment bank.


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