Paper: arXiv 2503.08692

Abstract

We propose a simple yet robust unsupervised model to detect pump-and-dump events on tokens listed on the Poloniex Exchange platform. By combining threshold-based criteria with exponentially weighted moving averages (EWMA) and volatility measures, our approach effectively distinguishes genuine anomalies from minor trading fluctuations, even for tokens with low liquidity and prolonged inactivity. These characteristics present a unique challenge, as standard anomaly-detection methods often over-flag negligible volume spikes. Our framework overcomes this issue by tailoring both price and volume thresholds to the specific trading patterns observed, resulting in a model that balances high true-positive detection with minimal noise.

Complexity vs Empirical Score

  • Math Complexity: 2.0/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses basic statistical concepts like exponentially weighted moving averages and volatility, but lacks heavy derivations or advanced mathematics. It is heavily data-driven with a focus on real-world exchange data and practical implementation.

Research Flowchart

  flowchart TD
  A["Research Goal"] -->|Identify anomalous pump-and-dump events in low-liquidity crypto| B["Data & Inputs"]
  B --> C["Poloniex Exchange Data<br/>Price & Volume Time Series"]
  C --> D["Methodology: Preprocessing"]
  D --> E["EWMA & Volatility Calculation"]
  E --> F["Threshold-Based Detection<br/>Adaptive Price/Volume Filters"]
  F --> G["Outcome"]
  G --> H["High True-Positive Detection<br/>Minimized False Positives"]
  G --> I["Robust against Market Noise<br/>Effective for Low Liquidity Tokens"]

Cited References (24)

  1. Cryptocurrency pump and dump schemes: Quantification and detection. (2019)
  2. To the moon: Analyzing cryptocurrency pump-and-dump schemes. (2018)
  3. The anatomy of a pump-and-dump scheme. (2019)
  4. The 2024 Crypto Crime Report. (2024)
  5. Guidance for a Risk-Based Approach to Virtual Assets and Virtual Asset Service Providers. (2019)
  6. Securities Exchange Act of 1934. (1934)
  7. Cryptocurrency Pump and Dump Schemes: Quantification and Detection (2023)
  8. Cryptocurrency Pump and Dump Schemes: Quantification and Detection (2019)
and 16 more
  1. Hybrid Cryptocurrency Pump and Dump Detection (2020)
  2. Machine Learning-Based Detection of Pump-and-Dump Schemes in Real-Time (2024) — link
  3. Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump (2023)
  4. Pump-and-Dump Schemes in Financial Markets: Evidence and Analysis (2008)
  5. Pump, Dump, and then What? The Long-Term Impact of Cryptocurrency Pump-and-Dump Schemes (2023) — link
  6. Fraud, Regulation and Enforcement: An International Perspective on Market Manipulation (2015)
  7. Information Asymmetry and Fraudulent Trading: Evidence from Pump-and-Dump Schemes (2010)
  8. Exploratory Data Analysis (EDA) for Banking and Finance: Unveiling Insights and Patterns (2024)
  9. Major Issues in High-Frequency Financial Data Analysis: A Survey of Solutions (2025)
  10. A Historical Analysis of Pump and Dump Schemes and Market Manipulation (1999)
  11. Price Manipulation in the Bitcoin Ecosystem (2018)
  12. Candlestick Patterns and their Role in Market Data Analysis (2012)
  13. Deep Learning for Anomaly Detection: A Survey (2019) — link
  14. Unsupervised Anomaly Detection in Time Series: Challenges and Opportunities (2020)
  15. Anomaly Detection: A Survey (2009)
  16. Conditional Anomaly Detection in Complex Data: Methods and Applications (2019)

0 of 24 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.