# The Anatomy of a Cryptocurrency Pump-and-Dump Scheme

arXiv: https://arxiv.org/abs/1811.10109 · HTML: https://mlsanity.com/paper/1811.10109/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2018.

## Built on by (most cited first, 15 of 15)

- [Applications of Signature Methods to Market Anomaly Detection](https://mlsanity.com/paper/2201.02441/) (2022)
- [Uncovering the Dark Side of Telegram: Fakes, Clones, Scams, and Conspiracy Movements](https://mlsanity.com/paper/2111.13530/) (2021)
- [When Hallucination Costs Millions: Benchmarking AI Agents in High-Stakes Adversarial Financial Markets](https://mlsanity.com/paper/2510.00332/) (2025)
- [Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump](https://mlsanity.com/paper/2204.12929/) (2022)
- [textscPerseus: Tracing the Masterminds Behind Cryptocurrency Pump-and-Dump Schemes](https://mlsanity.com/paper/2503.01686/) (2025)
- [PreScam: A Benchmark for Predicting Scam Progression from Early Conversations](https://mlsanity.com/paper/2605.12243/) (2026)
- [Paper Agents, Paper Gains: An Empirical Analysis of DeFi Investment Agents](https://mlsanity.com/paper/2605.29174/) (2026)
- [Probabilistic Sampling-Enhanced Temporal-Spatial GCN: A Scalable Framework for Transaction Anomaly Detection in Ethereum Networks](https://mlsanity.com/paper/2310.00144/) (2023)
- [Slow is Fast! Dissecting Ethereum's Slow Liquidity Drain Scams](https://mlsanity.com/paper/2503.04850/) (2025)
- [Taming Data Challenges in ML-based Security Tasks Using Generative AI](https://mlsanity.com/paper/2507.06092/) (2025)
- [The Tag is the Signal: URL-Agnostic Credibility Scoring for Messages on Telegram](https://mlsanity.com/paper/2601.13294/) (2026)
- [MELT: A Behavioral Trace Dataset for High-Risk Memecoin Launch Detection](https://mlsanity.com/paper/2602.13480/) (2026)
- [Fraud Detection in Cryptocurrency Markets with Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2604.24590/) (2026)
- [PumpSense: Real-Time Detection and Target Extraction of Crypto Pump-and-Dumps on Telegram](https://mlsanity.com/paper/2605.09431/) (2026)
- [Your Agent Says Yes: Interpreting Adversarial Market Behavior Beyond Individual Transactions](https://mlsanity.com/paper/2609.07675/) (2026)

## Abstract (arXiv)

While pump-and-dump schemes have attracted the attention of cryptocurrency observers and regulators alike, this paper represents the first detailed empirical query of pump-and-dump activities in cryptocurrency markets. We present a case study of a recent pump-and-dump event, investigate 412 pump-and-dump activities organized in Telegram channels from June 17, 2018 to February 26, 2019, and discover patterns in crypto-markets associated with pump-and-dump schemes. We then build a model that predicts the pump likelihood of all coins listed in a crypto-exchange prior to a pump. The model exhibits high precision as well as robustness, and can be used to create a simple, yet very effective trading strategy, which we empirically demonstrate can generate a return as high as 60% on small retail investments within a span of two and half months. The study provides a proof of concept for strategic crypto-trading and sheds light on the application of machine learning for crime detection.