Paper: arXiv 2311.18717

Abstract

Existing studies on crypto wash trading often use indirect statistical methods or leaked private data, both with inherent limitations. This paper leverages public on-chain NFT data for a more direct and granular estimation. Analyzing three major exchanges, we find that ~38% (30-40%) of trades and ~60% (25-95%) of traded value likely involve manipulation, with significant variation across exchanges. This direct evidence enables a critical reassessment of existing indirect methods, identifying roundedness-based regressions à la Cong et al. (2023) as most promising, though still error-prone in the NFT setting. To address this, we develop an AI-based estimator that integrates these regressions in a machine learning framework, significantly reducing both exchange- and trade-level estimation errors in NFT markets (and beyond).

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced statistical methods and machine learning (boosting, deep learning) with hyperparameter optimization, indicating moderate-to-high math complexity. It is highly empirical, using massive public on-chain NFT data from three exchanges, implementing direct filters, and reporting specific performance metrics (AUC scores, error rates), making it data- and implementation-heavy.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Can AI directly detect NFT wash trading using<br>public on-chain data?"] --> B["Methodology<br>Empirical analysis of NFT trades across<br>3 major exchanges (NFT marketplace data)"]
  
  B --> C["Computational Process<br>1. Baseline: Statistical models (e.g., roundedness regressions)<br>2. AI Estimator: Machine learning framework integrating<br>regressions to correct exchange & trade-level errors"]
  
  C --> D["Key Findings/Outcomes<br>• Direct Evidence: ~38% of trades / ~60% of value manipulated<br>• Exchange variation: significant differences detected<br>• ML estimator significantly reduces estimation errors vs.<br>traditional statistical methods<br>• Public data enables granular, repeatable analysis"]
  
  style A fill:#e1f5e1
  style D fill:#fff2cc

Cited References (22)

  1. Tabnet: Attentive interpretable tabular learning (2021)
  2. Using machine learning Meta-Classifiers to detect financial frauds (2022) — link
  3. Blur continues momentum with incentive program (2023) — link
  4. Detecting wash trade in the financial market (2014) — link
  5. Most Popular NFT Marketplaces by Market Share (2023)
  6. Crypto Wash Trading (2023) — link
  7. How Much Wash Trading Is Really Happening on Blur? (2023) — link
  8. Pricing Dynamics and Herding Behavior of NFTs (2023) — link
and 14 more
  1. NFT Wash Trading on Ethereum ( 2022)
  2. Selection-Neglect in the NFT Bubble (2023) — link
  3. Fraud Transaction Recognition: A Money Flow Network Approach (2015) — link
  4. Ethereum ETL ( 2023 )
  5. A Game of NFTs : Characterizing NFT Wash Trading in the Ethereum Blockchain (2023) — link
  6. What’s Not There: Odd Lots and Market Data (2014)
  7. DEXs Gain Market Share As Faith In Centralized Crypto Players Erodes (2022) — link
  8. NFT Wash Trading: Quantifying suspicious behaviour in NFT markets (2022) — link
  9. NFT Wash Trading (2023)
  10. Direct Evidence of Bitcoin Wash Trading (2024) — link
  11. Trading volume manipulation and competition among centralized crypto exchanges (2020)
  12. The Dark Side of NFTs: A Large-Scale Empirical Study of Wash Trading (2024) — link
  13. Market Manipulation in Non-Fungible Token Markets (2024)
  14. Tax-Loss Harvesting with Cryptocurrencies (2021)

0 of 22 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.