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

Qiangqiang Liu; Qian Huang; Frank Fan; Haishan Wu; Xueyan Tang

Paper 2025/927

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

Qiangqiang Liu, Risk Department Binance Dubai, United Arab Emirates

Qian Huang, Risk Department Binance Hong Kong, China

Frank Fan, Risk Department Binance Hong Kong, China

Haishan Wu, AI Department Zand Dubai, United Arab Emirates

Xueyan Tang, Suzhou Artificial Intelligence Research Institute Shanghai Jiao Tong University Suzhou, Jiangsu, China

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.

Note: IEEE International Conference on Blockchain and Cryptocurrency (Proc. IEEE ICBC 2025)

Metadata

Available format(s): PDF
Category: Applications
Publication info: Published elsewhere. IEEE ICBC
Keywords: meme tokens liquidity risk blockchain analysis entity identification
Contact author(s): codi l @ binance com
qian huang @ binance com
frank f @ binance com
haishan wu @ zand ae
mirror tang @ alumni stanford edu
History: 2025-05-23: approved; 2025-05-22: received; See all versions
Short URL: https://ia.cr/2025/927
License: CC BY

BibTeX

@misc{cryptoeprint:2025/927,
      author = {Qiangqiang Liu and Qian Huang and Frank Fan and Haishan Wu and Xueyan Tang},
      title = {Enhancing Meme Token Market Transparency: A Multi-Dimensional Entity-Linked Address Analysis for Liquidity Risk Evaluation},
      howpublished = {Cryptology {ePrint} Archive, Paper 2025/927},
      year = {2025},
      url = {https://eprint.iacr.org/2025/927}
}