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Title: A machine learning attack on illegal trading Authors:  Artem Prokhorov - University of Sydney (Australia) [presenting]
Henry Leung - Sydney University (Australia)
Robert James - The University of Sydney (Australia)
Abstract: An adaptive framework is designed for the detection of illegal trading behavior. Its key component is an extension of a pattern recognition tool, originating from the field of signal processing and adapted to modern electronic systems of securities trading. The new method combines the flexibility of dynamic time warping with contemporary approaches from extreme value theory to explore large-scale transaction data and accurately identify illegal trading patterns. Importantly, our method does not need access to any confirmed illegal transactions for training. We use a high-frequency order book dataset provided by an international investment firm to show that the method achieves remarkable improvements over alternative approaches in the identification of suspected illegal insider trading cases.