Microstructure and Manipulation: Quantifying Pump-and-Dump Dynamics in Cryptocurrency Markets

ArXiv ID: 2504.15790 “View on arXiv”

Authors: Unknown

Abstract

Building on our prior threshold-based analysis of six months of Poloniex trading data, we have extended both the temporal span and granularity of our study by incorporating minute-level OHLCV records for 1021 tokens around each confirmed pump-and-dump event. First, we algorithmically identify the accumulation phase, marking the initial and final insider volume spikes, and observe that 70% of pre-event volume transacts within one hour of the pump announcement. Second, we compute conservative lower bounds on insider profits under both a single-point liquidation at 70% of peak and a tranche-based strategy (selling 20% at 50%, 30% at 60%, and 50% at 80% of peak), yielding median returns above 100% and upper-quartile returns exceeding 2000%. Third, by unfolding the full pump structure and integrating social-media verification (e.g., Telegram announcements), we confirm numerous additional events that eluded our initial model. We also categorize schemes into “pre-accumulation” versus “on-the-spot” archetypes-insights that sharpen detection algorithms, inform risk assessments, and underpin actionable strategies for real-time market-integrity enforcement.

Keywords: Pump and Dump, Market Manipulation, High-Frequency Data, Insider Trading, Event Study, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper employs relatively simple statistical measures (thresholds, averages, distributions) and algorithmic logic rather than advanced stochastic calculus or heavy derivations, but it is heavily data-driven with minute-level OHLCV records, multi-event analysis, and concrete profit quantification, making it backtest-ready and implementation-heavy.

  flowchart TD
    A["Research Goal: Quantify Pump-and-Dump Dynamics"] --> B["Data Input<br>1021 Tokens: Minute-Level OHLCV"]
    B --> C["Methodology: Event Study<br>Identify Pumps via Volume Spikes"]
    C --> D["Computational Process A<br>Estimate Insider Profits"]
    C --> E["Computational Process B<br>Identify Accumulation Phase<br>Verify via Social Media"]
    D --> F["Key Findings"]
    E --> F
    subgraph F ["Outcomes"]
        F1["Median Returns > 100%"]
        F2["High Returns via Tranche Selling<br>(Sell at 50%, 60%, 80% of Peak)"]
        F3["70% of Pre-Event Volume in 1 Hour"]
        F4["Categorized: Pre-accumulation vs On-the-spot"]
    end