Paper: arXiv 2303.06051

Authors: Andrea Barbon, Angelo Ranaldo

Abstract

By investigating nonfungible tokens (NFTs), we provide the first systematic study of retail investor behavior through asset bubbles. Given that NFTs are recorded in public blockchains, we are able to track investor behavior over time, leading to the identification of numerous price run-ups and crashes. Our study reveals that agent-level variables, such as investor sophistication, heterogeneity, and wash trading, in addition to aggregate variables, such as volatility, price acceleration, and turnover, significantly predict bubble formation and price crashes. We find that sophisticated investors consistently outperform others and exhibit characteristics consistent with superior information and skills, supporting the narrative surrounding asset pricing bubbles.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 6.0/10 (capped from 8.0: no out-of-sample test; see penalty flags)
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: This paper presents a novel empirical study on retail investor behavior and bubble dynamics using granular on-chain NFT data. While the mathematical modeling is relatively light, the empirical rigor and originality of tracking agent-level wallet behavior through price crashes are strong.

Research Flowchart

  flowchart TD
    A[Research Goal: Predict NFT Asset Bubbles & Analyze Investor Behavior] --> B[Data Input: On-Chain Public Blockchain Records]
    B --> C[Identify Price Run-ups & Crashes]
    C --> D[Calculate Aggregate & Agent-Level Variables]
    D --> E[Statistical & Predictive Modeling]
    E --> F[Key Findings: Volatility & Wash Trading Predict Crashes]
    E --> G[Key Findings: Sophisticated Investors Consistently Outperform]