How Wash Traders Exploit Market Conditions in Cryptocurrency Markets

ArXiv ID: 2411.08720 “View on arXiv”

Authors: Unknown

Abstract

Wash trading, the practice of simultaneously placing buy and sell orders for the same asset to inflate trading volume, has been prevalent in cryptocurrency markets. This paper investigates whether wash traders in Bitcoin act deliberately to exploit market conditions and identifies the characteristics of such manipulative behavior. Using a unique dataset of 18 million transactions from Mt. Gox, once the largest Bitcoin exchange, I find that wash trading intensifies when legitimate trading volume is low and diminishes when it is high, indicating strategic timing to maximize impact in less liquid markets. The activity also exhibits spillover effects across platforms and decreases when trading volumes in other asset classes like stocks or gold rise, suggesting sensitivity to broader market dynamics. Additionally, wash traders exploit periods of heightened media attention and online rumors to amplify their influence, causing rapid but short-lived spikes in legitimate trading volume. Using an exogenous demand shock associated with illicit online marketplaces, I find that wash trading responds to contemporaneous events affecting Bitcoin demand. These results advance the understanding of manipulative practices in digital currency markets and have significant implications for regulators aiming to detect and prevent wash trading.

Keywords: Wash Trading, Market Manipulation, High-Frequency Trading Data, Market Microstructure, Liquidity Analysis, Cryptocurrencies (Bitcoin)

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper uses extensive empirical analysis with a unique 18 million transaction dataset, exogenous events, and spillover analysis, demonstrating high data/implementation rigor, but relies on statistical and econometric methods rather than advanced mathematical derivations or formulas.

  flowchart TD
    A["Research Goal<br>Identify Wash Trading<br>in Bitcoin &<br>Exploit Market Conditions"] --> B["Data Source<br>18M High-Frequency Transactions<br>from Mt. Gox"]
    B --> C["Methodology<br>Event Studies &<br>Correlation Analysis"]
    C --> D{"Computational Analysis"}
    D --> E["Liquidity Impact<br>High Wash Vol in Low<br>Legit Volume Periods"]
    D --> F["Market Dynamics<br>Sensitivity to Stocks/Gold<br>Cross-Platform Spillover"]
    D --> G["Exogenous Shocks<br>Response to Illicit<br>Demand Spikes"]
    E & F & G --> H["Key Outcomes<br>Strategic Timing &<br>Regulatory Implications"]