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A machine learning attack on illegal trading
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DOI：10.1016/j.jbankfin.2022.106735.png)
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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 transac-tion 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.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Illegal trading
Insider trading
Market manipulation
Market micro-structure
Order book
Machine learning
期刊
J
IF:
3.8
论文数:
6.4K
被引数:
2.4W
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