🤖 AI Summary
Relying solely on Total Value Locked (TVL) is insufficient for accurately assessing the true risk of tokenized real-world assets (RWAs), as it overlooks critical vulnerabilities such as illiquidity, holder concentration, and poor market quality. This work proposes the first multidimensional, interpretable risk assessment framework that transcends TVL by evaluating RWAs along three dimensions—liquidity (L), concentration (C), and market quality (M)—using on-chain public data. The framework constructs a composite risk score incorporating the Herfindahl-Hirschman Index to measure holder concentration, alongside metrics such as transaction frequency, active address count, and turnover ratio. Empirical analysis identifies several RWA tokens exhibiting high TVL yet elevated risk, effectively uncovering hazards obscured by TVL alone and establishing a transparent, comparable benchmark for evaluating tokenized assets.
📝 Abstract
Tokenized real-world assets (RWAs) are often evaluated through headline indicators such as total value locked (TVL) or on-chain asset value. However, a large asset base does not necessarily imply low risk, since tokenized assets may remain illiquid, weakly traded, or highly concentrated among a small number of holders. Using public data from RWA.xyz, this paper develops an empirical and explainable risk scoring framework for tokenized RWA markets. The framework evaluates three dimensions of risk: liquidity risk $L$, concentration risk $C$, and market-quality risk $M$. These risk dimensions are constructed from observable indicators, including turnover, holder distribution, active-address activity, transfer frequency, and network concentration measured through Herfindahl indices. The analysis shows that several RWA tokens with substantial on-chain value exhibit high empirical risk because they combine limited transfer activity, low turnover, and concentrated ownership structures. In contrast, assets with broader participation and stronger on-chain activity display lower liquidity and concentration risk, even when their headline asset values are smaller. The findings demonstrate that TVL alone can obscure important risks in tokenized asset markets. By providing a transparent and data-driven risk scoring approach, this paper contributes to the empirical assessment of RWA liquidity and offers a practical basis for comparing tokenized assets beyond headline valuation metrics.