A Midsummer Meme's Dream: Investigating Market Manipulations in the Meme Coin Ecosystem

Paper: arXiv 2507.01963

Abstract

From viral jokes to a billion-dollar phenomenon, meme coins have become one of the most popular segments in cryptocurrency markets. Unlike utility-focused crypto assets like Bitcoin, meme coins derive value primarily from community sentiment, making them vulnerable to manipulation. This study presents an unprecedented cross-chain analysis of the meme coin ecosystem, examining 34,988 tokens across Ethereum, BNB Smart Chain, Solana, and Base. We characterize their tokenomics and track their growth in a three-month longitudinal analysis. We discover that among high-return tokens (>100%), an alarming 82.8% show evidence of artificial growth strategies designed to create a misleading appearance of market interest. These include wash trading and a new form of manipulation we define as Liquidity Pool-Based Price Inflation (LPI), where small strategic purchases trigger dramatic price increases. We find that profit extraction schemes, such as pump and dumps and rug pulls, typically follow initial manipulations like wash trading or LPI, indicating how early manipulations create the foundation for later exploitation. We quantify the economic impact of these schemes, identifying over 17,000 victimized addresses with realized losses exceeding $9.3 million. These findings reveal that combined manipulations are widespread among high-performing meme coins, suggesting that their dramatic gains are often driven by coordinated efforts rather than natural market dynamics.

Complexity vs Empirical Score

• Math Complexity: 1.0/10
• Empirical Rigor: 9.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper presents a large-scale empirical analysis with a curated dataset, clear detection methodologies, and quantified financial impact, requiring minimal advanced mathematics. Its focus is on data processing and real-world findings rather than theoretical derivations.

Research Flowchart

  flowchart TD
  A["Research Goal: Investigate Market Manipulations<br>in the Meme Coin Ecosystem"] --> B["Data Collection & Scope"]
  B --> C["Longitudinal Analysis"]
  C --> D["Computational Detection"]
  D --> E["Key Outcomes & Findings"]
  
  subgraph B ["Data/Inputs"]
      B1["34,988 Tokens"]
      B2["4 Chains: Ethereum, BNB, Solana, Base"]
      B3["3-Month Period"]
  end
  
  subgraph C ["Methodology"]
      C1["Tokenomics Characterization"]
      C2["Growth Tracking"]
  end
  
  subgraph D ["Analysis"]
      D1["Wash Trading Detection"]
      D2["LPI Identification<br>Liquidity Pool-Based Price Inflation"]
      D3["Exploit Pattern Mapping<br>Pump & Dump / Rug Pull"]
  end
  
  subgraph E ["Findings"]
      E1["82.8% of High-Return Tokens<br>use Artificial Growth Strategies"]
      E2["17,000+ Victim Addresses"]
      E3["$9.3M+ Realized Losses"]
      E4["Manipulations form Foundation<br>for Profit Extraction"]
  end

Cited References (81)

1. Token Spammers, Rug Pulls, and Sniper Bots: An Analysis of the Ecosystem of Tokens in Ethereum and in the Binance Smart Chain (BNB)) (2023)
2. Regulation (EU) 2023/1114 on markets in crypto-assets (MiCA) – Article 91: Prohibition of market manipulation (2023)
3. Use of ranks in one-criterion variance analysis (1952)
4. Multiple comparisons among means (1961)
5. Memes in digital culture (2013)
6. On the origins of memes by means of fringe web communities (2018)
7. Using tf-idf to determine word relevance in document queries (2003)
8. EBK-means: A clustering technique based on elbow method and k-means in WSN (2014)

0 of 81 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.