A machine learning attack on illegal trading

Robert James, Henry Leung and Artem Prokhorov

Journal of Banking & Finance, 2023, vol. 148, issue C

Abstract: 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.

Keywords: Illegal trading; Insider trading; Market manipulation; Market micro-structure; Order book; Machine learning (search for similar items in EconPapers)
JEL-codes: C55 G14 G18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:148:y:2023:i:c:s0378426622003156

DOI: 10.1016/j.jbankfin.2022.106735

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