Predicting the success of new crypto-tokens: the Pump.fun case
Abstract
We study the dynamics of token launched on Pump.fun, a Solana-based launchpad platform, to identify the determinants of the token success. Pump.fun employs a bonding curve mechanism to bootstrap initial liquidity possibly leading to graduation to the on-chain market, which can be seen as a token success. We build predictive models of the probability of graduation conditional on the current amount of Solana locked in the bonding curve and a set of explanatory variables that capture structural and behavioral aspects of the launch process. Conditioning the graduation probability on these variables significantly improves its predictive power, providing insights into early-stage market behavior, speculative and manipulative dynamics, and the informational efficiency of bonding-curve-based token launches.
Keywords
Cite
@article{arxiv.2602.14860,
title = {Predicting the success of new crypto-tokens: the Pump.fun case},
author = {Giulio Marino and Manuel Naviglio and Francesco Tarantelli and Fabrizio Lillo},
journal= {arXiv preprint arXiv:2602.14860},
year = {2026}
}