Enhancing Meme Token Market Transparency: A Multi-Dimensional Entity-Linked Address Analysis for Liquidity Risk Evaluation

ArXiv ID: 2506.05359 “View on arXiv”

Authors: Qiangqiang Liu, Qian Huang, Frank Fan, Haishan Wu, Xueyan Tang

Abstract

Meme tokens represent a distinctive asset class within the cryptocurrency ecosystem, characterized by high community engagement, significant market volatility, and heightened vulnerability to market manipulation. This paper introduces an innovative approach to assessing liquidity risk in meme token markets using entity-linked address identification techniques. We propose a multi-dimensional method integrating fund flow analysis, behavioral similarity, and anomalous transaction detection to identify related addresses. We develop a comprehensive set of liquidity risk indicators tailored for meme tokens, covering token distribution, trading activity, and liquidity metrics. Empirical analysis of tokens like BabyBonk, NMT, and BonkFork validates our approach, revealing significant disparities between apparent and actual liquidity in meme token markets. The findings of this study provide significant empirical evidence for market participants and regulatory authorities, laying a theoretical foundation for building a more transparent and robust meme token ecosystem.

Keywords: Entity-Linked Address Identification, Fund Flow Analysis, Liquidity Risk Indicators, Market Manipulation, Cryptocurrency, Cryptocurrency

Complexity vs Empirical Score

  • Math Complexity: 2.0/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Street Traders
  • Why: The paper focuses on practical entity-linked address clustering using graph mining and data labeling from APIs like BscScan, with empirical validation on specific tokens (BabyBonk, NMT, BonkFork) rather than theoretical derivations. It proposes actionable liquidity risk indicators derived from transaction data, making it highly implementation-heavy and backtest-ready.
  flowchart TD
    A["Research Goal<br>Liquidity Risk Evaluation<br>in Meme Token Markets"] --> B["Data Collection<br>Token Transactions & Wallet Data"]
    B --> C["Methodology<br>Entity-Linked Address Analysis"]
    C --> D["Fund Flow & Behavioral Similarity"]
    D --> E["Anomalous Transaction Detection"]
    E --> F["Computation<br>Liquidity Risk Indicators"]
    F --> G["Findings<br>Disparity: Apparent vs Actual Liquidity"]
    G --> H["Outcome<br>Enhanced Market Transparency & Risk Evaluation"]