Tokenize Everything, But Can You Sell It? RWA Liquidity Challenges and the Road Ahead

ArXiv ID: 2508.11651 “View on arXiv”

Authors: Rischan Mafrur

Abstract

The tokenization of real-world assets (RWAs) promises to transform financial markets by enabling fractional ownership, global accessibility, and programmable settlement of traditionally illiquid assets such as real estate, private credit, and government bonds. While technical progress has been rapid, with over $25 billion in tokenized RWAs brought on-chain as of 2025, liquidity remains a critical bottleneck. This paper investigates the gap between tokenization and tradability, drawing on recent academic research and market data from platforms such as RWA.xyz. We document that most RWA tokens exhibit low trading volumes, long holding periods, and limited investor participation, despite their potential for 24/7 global markets. Through case studies of tokenized real estate, private credit, and tokenized treasury funds, we present empirical liquidity observations that reveal low transfer activity, limited active address counts, and minimal secondary trading for most tokenized asset classes. Next, we categorize the structural barriers to liquidity, including regulatory gating, custodial concentration, whitelisting, valuation opacity, and lack of decentralized trading venues. Finally, we propose actionable pathways to improve liquidity, ranging from hybrid market structures and collateral-based liquidity to transparency enhancements and compliance innovation. Our findings contribute to the growing discourse on digital asset market microstructure and highlight that realizing the liquidity potential of RWAs requires coordinated progress across legal, technical, and institutional domains.

Keywords: Tokenization, Market Liquidity, Digital Assets, Fractional Ownership, Market Microstructure

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Philosophers
• Why: The paper relies on qualitative analysis, market data summaries, and case studies with no complex mathematical models or heavy formulas. Its empirical observations are data-informed but lack backtesting or statistical rigor.

  flowchart TD
    A["Research Goal: Investigate the liquidity gap<br>between RWA tokenization and tradability"] --> B["Methodology: Multi-Faceted Case Study Analysis"]
    
    B --> C["Data Sources & Inputs<br>RWA.xyz market data<br>Academic research<br>Tokenized real estate, credit, & treasury case studies"]
    
    C --> D["Computational Process: Liquidity Observation"]
    D --> E["Key Findings: Liquidity Challenges<br>Low trading volumes & transfer activity<br>Limited active addresses<br>Minimal secondary trading"]
    
    D --> F["Key Findings: Structural Barriers<br>Regulatory gating & custodial concentration<br>Whitelisting requirements<br>Valuation opacity<br>Lack of decentralized venues"]
    
    E --> G["Outcomes: Actionable Pathways<br>Hybrid market structures<br>Collateral-based liquidity solutions<br>Enhanced transparency frameworks<br>Compliance innovation"]
    
    F --> G