Paper: arXiv 2305.01543

Authors: Derek Liu, Francesco Piccoli, Katie Chen, Adrina Tang, Victor Fang

Abstract

Wash trading is a form of market manipulation where the same entity sells an asset to themselves to drive up market prices, launder money under the cover of a legitimate transaction, or claim a tax loss without losing ownership of an asset. Although the practice is illegal with traditional assets, lack of supervision in the non-fungible token market enables criminals to wash trade and scam unsuspecting buyers while operating under regulators radar. AnChain.AI designed an algorithm that flags transactions within an NFT collection history as wash trades when a wallet repurchases a token within 30 days of previously selling it. The algorithm also identifies intermediate transactions within a wash trade cycle. Testing on 7 popular NFT collections reveals that on average, 0.14% of transactions, 0.11% of wallets, and 0.16% of tokens in each collection are involved in wash trading. These wash trades generate an overall total price manipulation, sales, and repurchase profit of $900K, $1.1M, and negative $1.6M respectively. The results draw attention to the prevalent market manipulation taking place and inform unsuspecting buyers which tokens and sellers may be involved in criminal activity.

Complexity vs Empirical Score

  • Math Complexity: 2.0/10
  • Empirical Rigor: 4.5/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper presents an intuitive rule-based heuristic for identifying wash trading in NFTs rather than advanced mathematical modeling. While it offers practical descriptive statistics across seven collections, the empirical framework lacks formal statistical controls and rigorous validation methodologies.

Research Flowchart

  flowchart TD
    A["Research Goal: Detect NFT Wash Trading & Market Manipulation"] --> B["Input: Transaction History of 7 Popular NFT Collections"]
    B --> C["Apply AnChain.AI Detection Algorithm"]
    C --> D{"Graph & Wallet Tracking Analysis"}
    D -- Identify Repurchases < 30 Days --> E["Flag Wash Trade Cycles & Intermediate Transactions"]
    E --> F["Key Findings: 0.14% Trans, 0.11% Wallets, 0.16% Tokens Affected"]
    E --> G["Financial Impact: $900K Price Manipulation, $1.1M Sales, -$1.6M Profit"]