Paper: arXiv 2610.39458

Authors: Fail Gafarov

Abstract

We study coordinated groups of wallets on a bonding-curve launchpad (pump.fun on Solana), using a complete record of 200,443 token launches, 15,854,121 curve trades and 51,025,958 SOL transfers over 88.3 hours. A prospective, multi-channel discovery procedure (co-buying, shared funding, joint signing, transfers, consolidation) yields 23,710 wallet groups. Without ground-truth labels we validate them by out-of-sample link prediction, independent non-trading links and a comparison with naive co-buy clustering at equal numbers of flagged tokens: raw clustering collapses into one giant component, yet it ranks tokens at least as well as the groups, so the value of the structure is descriptive. Group activity is highly concentrated and splits into serial creators and long-lived rings of early buyers. Known members buy earlier than other buyers of the same token and extract more cash than an activity-matched placebo set, while late buyers’ net cash flow is negative; tokens with members show a pump-like price path, but the contrast vanishes when controls are restricted to comparable tokens, so we report it as an association. A follower entering at the earliest group signal loses money out of sample in all 32 configurations and does no better than random entry. A few pools of tens to over a hundred fast wallets are associated with a two- to four-fold higher chance of reaching 20 SOL, also within creators, with a smaller excess at 60 SOL and none for graduation; on a final hold-out the team rules keep a precision of about one half to three quarters, yet many signals arrive when the curve already holds 20 SOL, one team supplies most of them, and they add no information beyond early curve state. We also document evaluation errors that turn the same data into apparent profit.

Complexity vs Empirical Score

  • Math Complexity: 5.0/10
  • Empirical Rigor: 9.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper demonstrates strong empirical rigor through extensive data analysis and robust validation methods for wallet groups. Its novelty lies in the multi-channel discovery procedure and the detailed, population-level study of coordinated activity on a specific blockchain launchpad. While the mathematical complexity is moderate, the statistical methodologies are advanced and well-applied.

Research Flowchart

  flowchart TD
    A[Research Goal: Study Coordinated Wallet Groups on pump.fun] --> B{Data Collection: 200k launches, 15M trades, 51M SOL transfers (88.3 hrs)}
    B --> C[Methodology: Multi-channel Wallet Group Discovery (co-buying, funding, signing, transfers)]
    C --> D{Computational Process: Yields 23,710 Wallet Groups}
    D --> E[Validation: Out-of-sample link prediction, non-trading links, comparison with naive clustering]
    E --> F{Key Findings: Group Activity & Impact}
    F --> G1[G1: Groups split into serial creators & long-lived early buyer rings]
    F --> G2[G2: Members buy earlier, extract more cash (late buyers lose money)]
    F --> G3[G3: Tokens with members show pump-like path (association, vanishes with controls)]
    F --> G4[G4: Follower entering at earliest group signal loses money]
    F --> G5[G5: Fast wallets associated with higher chance of reaching 20 SOL (precision 1/2-3/4)]