We design an adaptive framework 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.
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