Paper: arXiv 2611.11436

Authors: Fail Gafarov

Abstract

If the right token-creation teams had been known, how much could following them have returned, and what is left when only information available before each launch is used? We study ten separately recorded Pumpfun windows (April 2024 to September 2026), each reconstructed independently from real-SOL reserve states and finite-capital accounts that start with one SOL. Teams are directed funding groups ranked in hindsight. When every team exits at its own best reserve threshold, chosen in hindsight, the main account ends above one SOL in all 8 windows that have a qualifying team (1.10 to 11.32 SOL), but free cash exceeds one SOL in only 5, and marks of positions that never reached their threshold make up 17–94% of equity. If teams are recognised online, from tokens whose first hour has completed, 9 of 19 teams become identifiable only after their last launch; the remaining 10 end above one SOL in 10 cases, with free cash above one SOL in 7; funding links in this step still come from the full window, so even these results are not a fully realisable signal. A large model balance, a result above random cohorts that repeat the threshold search, and a statistical rejection in the selection-matched nulls (3 of 40 tests after Holm adjustment) are three different statements, and these nulls condition on the discovered funding graph, so they do not calibrate the whole selection procedure. Midpoint tests describe a further source of apparent opportunity. A detailed analysis of the best and the worst window, with every analysis parameter varied, shows that the worst result, which is close to break-even, rises several-fold with a stop rule or another funding-graph construction. The results are conditional retrospective model accounts, not executable profits, and not evidence of a causal effect of the SOL price.

Complexity vs Empirical Score

  • Math Complexity: 5.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper demonstrates strong empirical rigor through detailed retrospective analysis and statistical testing, combined with a moderate level of mathematical detail in its model accounts and funding network construction. Its novelty lies in applying funding network analysis to Pump.fun meme coins, offering new insights into team-following profitability.

Research Flowchart

  flowchart TD
    A[Research Goal: Quantify Team-Following Profitability in Pumpfun Meme Coins] --> B{Key Methodology: Retrospective Analysis of Funding Networks};
    B --> C[Data/Inputs: 10 Pumpfun Windows (Apr 2024 - Sep 2026), Real-SOL Reserve States, Finite-Capital Accounts (1 SOL initial)];
    C --> D{Computational Processes:
        1. Hindsight-Optimized Team Exits;
        2. Online Team Identification (first hour);
        3. Statistical Rejection Tests (Holm adjustment);
        4. Midpoint Tests;
        5. Sensitivity Analysis (best/worst window, stop rules, graph construction)};
    D --> E[Key Findings:
        - Hindsight-Optimized: 8/8 qualifying teams >1 SOL (1.10-11.32 SOL), 5/8 free cash >1 SOL;
        - Online Identification: 10/10 teams >1 SOL, 7/10 free cash >1 SOL (partial signal);
        - Statistical Rejection: 3/40 tests (Holm-adjusted);
        - Conditional Retrospective Model Accounts, Not Executable Profits];