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arXiv · 2412.18848

Machine Learning-Based Detection of Pump-and-Dump Schemes in Real-Time

Manuel Bolz·Kevin Brundler·Liam Kane·Panagiotis Patsias·Liam Tessendorf·Krzysztof Gogol·Taehoon Kim·Claudio Tessone
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Abstract

Cryptocurrency markets often face manipulation through prevalent pump-and-dump (P&D) schemes, where self-organized Telegram groups, some exceeding two million members, artificially inflate target cryptocurrency prices. These groups sell premium access to inside information, worsening information asymmetry and financial risks for subscribers and all investors. This paper presents a real-time prediction pipeline to forecast target coins and alert investors to possible P&D schemes. In a Poloniex case study, the model accurately identified the target coin among the top five from 50 random coins in 24 out of 43 (55.81%) P&D events. The pipeline uses advanced natural language processing (NLP) to classify Telegram messages, identifying 2,079 past pump events and detecting new ones in real-time.

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Manuel Bolz, Kevin Brundler, Liam Kane, Panagiotis Patsias, Liam Tessendorf, Krzysztof Gogol, Taehoon Kim, Claudio Tessone. 2024-12-25. Machine Learning-Based Detection of Pump-and-Dump Schemes in Real-Time. https://arxiv.org/abs/2412.18848

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