An Empirical Analysis of Scam Tokens on Ethereum Blockchain

ArXiv ID: 2402.19399 “View on arXiv”

Authors: Unknown

Abstract

This article presents an empirical investigation into the determinants of total revenue generated by counterfeit tokens on Uniswap. It offers a detailed overview of the counterfeit token fraud process, along with a systematic summary of characteristics associated with such fraudulent activities observed in Uniswap. The study primarily examines the relationship between revenue from counterfeit token scams and their defining characteristics, and analyzes the influence of market economic factors such as return on market capitalization and price return on Ethereum. Key findings include a significant increase in overall transactions of counterfeit tokens on their first day of fraud, and a rise in upfront fraud costs leading to corresponding increases in revenue. Furthermore, a negative correlation is identified between the total revenue of counterfeit tokens and the volatility of Ethereum market capitalization return, while price return volatility on Ethereum is found to have a positive impact on counterfeit token revenue, albeit requiring further investigation for a comprehensive understanding. Additionally, the number of subscribers for the real token correlates positively with the realized volume of scam tokens, indicating that a larger community following the legitimate token may inadvertently contribute to the visibility and success of counterfeit tokens. Conversely, the number of Telegram subscribers exhibits a negative impact on the realized volume of scam tokens, suggesting that a higher level of scrutiny or awareness within Telegram communities may act as a deterrent to fraudulent activities. Finally, the timing of when the scam token is introduced on the Ethereum blockchain may have a negative impact on its success. Notably, the cumulative amount scammed by only 42 counterfeit tokens amounted to almost 11214 Ether.

Keywords: Counterfeit Tokens, Uniswap, DeFi Fraud, Ethereum Market Factors, Tokenomics, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper is heavily empirical, relying on blockchain data collection, regression analysis of market factors, and specific results like transaction counts and Ether amounts, indicating high implementation readiness. However, the mathematics appears primarily descriptive and statistical (correlations, regressions) without advanced theoretical derivations or complex modeling.

  flowchart TD
    A["Research Goal: Determine factors<br>affecting scam token revenue"] --> B["Data Collection<br>Ethereum & Uniswap Data"]
    B --> C["Analysis: Regress Revenue<br>vs. Characteristics & Market Factors"]
    C --> D{"Key Findings"}
    D --> E["Positive Impact:<br>Upfront Fraud Cost, Legit Token Subscribers"]
    D --> F["Negative Impact:<br>Telegram Scrutiny, Scam Timing"]
    D --> G["Mixed/Market Impact:<br>Eth MCap Volatility, Price Return"]