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

TL;DR

This paper presents a novel multi-dimensional approach using entity-linked address analysis to evaluate liquidity risk in meme token markets, aiming to improve transparency and detect market vulnerabilities.

Contribution

It introduces an innovative method combining fund flow, behavioral, and anomaly analysis to identify related addresses and assess liquidity risk in meme tokens.

Findings

Empirical validation on tokens like BabyBonk, NMT, and BonkFork.

Reveals disparities between apparent and actual liquidity.

Provides a foundation for transparency in meme token markets.

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.