The Doge of Wall Street: Analysis and Detection of Pump and Dump Cryptocurrency Manipulations
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Abstract (arXiv)
Cryptocurrencies are increasingly popular. Even people who are not experts have started to invest in these assets, and nowadays, cryptocurrency exchanges process transactions for over 100 billion US dollars per month. Despite this, many cryptocurrencies have low liquidity and are highly prone to market manipulation. This paper performs an in-depth analysis of two market manipulations organized by communities over the Internet: The pump and dump and the crowd pump. The pump and dump scheme is a fraud as old as the stock market. Now, it got new vitality in the loosely regulated market of cryptocurrencies. Groups of highly coordinated people systematically arrange this scam, usually on Telegram and Discord. We monitored these groups for more than 3 years detecting around 900 individual events. We report on three case studies related to pump and dump groups. We leverage our unique dataset of the verified pump and dumps to build a machine learning model able to detect a pump and dump in 25 seconds from the moment it starts, achieving the results of 94.5% of F1-score. Then, we move on to the crowd pump, a new phenomenon that hit the news in the first months of 2021, when a Reddit community inflates the price of the GameStop stocks (GME) by over 1,900% on Wall Street, the world's largest stock exchange. Later, other Reddit communities replicate the operation on the cryptocurrency markets. The targets were DogeCoin (DOGE) and Ripple (XRP). We reconstruct how these operations developed and discuss differences and analogies with the standard pump and dump. We believe this study helps understand a widespread phenomenon affecting cryptocurrency markets. The detection algorithms we develop effectively detect these events in real-time and help investors stay out of the market when these frauds are in action.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 May 2021.
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Citing papers per year: 2022: 4; 2023: 0; 2024: 0; 2025: 1; 2026: 4.
9 papers.
- 2022Applications of Signature Methods to Market Anomaly Detectioncited by 9
- 2022Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dumpcited by 4
- 2025textscPerseus: Tracing the Masterminds Behind Cryptocurrency Pump-and-Dump Schemescited by 2
- 2022Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Pricescited by 1
- 2022A machine learning approach to support decision in insider trading detectioncited by 1
- 2026MELT: A Behavioral Trace Dataset for High-Risk Memecoin Launch Detectioncited by 0
- 2026PumpSense: Real-Time Detection and Target Extraction of Crypto Pump-and-Dumps on Telegramcited by 0
- 2026StakeBench: Evaluating Language Understanding Grounded in Market Commitmentcited by 0
- 2026Velocity- and Regime-Aware Detection of Intraday Options Market Manipulation, with Explainable Attributioncited by 0
- 2026Velocity- and Regime-Aware Detection of Intraday Options Market Manipulation, with Explainable Attribution5 Aug 2026
- 2026StakeBench: Evaluating Language Understanding Grounded in Market Commitment25 May 2026
- 2026PumpSense: Real-Time Detection and Target Extraction of Crypto Pump-and-Dumps on Telegram10 May 2026
- 2026MELT: A Behavioral Trace Dataset for High-Risk Memecoin Launch Detection13 Feb 2026
- 2025textscPerseus: Tracing the Masterminds Behind Cryptocurrency Pump-and-Dump Schemes3 Mar 2025
- 2022A machine learning approach to support decision in insider trading detection6 Dec 2022
- 2022Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump21 Apr 2022
- 2022Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Prices24 Jan 2022
- 2022Applications of Signature Methods to Market Anomaly Detection7 Jan 2022