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

TL;DR

This paper introduces a real-time machine learning pipeline that leverages NLP to detect and predict pump-and-dump schemes in cryptocurrency markets, aiming to alert investors and mitigate manipulation risks.

Contribution

It presents a novel real-time prediction system combining NLP and machine learning to identify pump-and-dump schemes in cryptocurrency markets.

Findings

Achieved 55.81% accuracy in identifying target coins during P&D events.

Successfully classified 2,079 past pump events using NLP.

Detected new pump-and-dump schemes in real-time.

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.