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arXiv · 2602.14860

Predicting the success of new crypto-tokens: the Pump.fun case

Giulio Marino·Manuel Naviglio·Francesco Tarantelli·Fabrizio Lillo
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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.

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Giulio Marino, Manuel Naviglio, Francesco Tarantelli, Fabrizio Lillo. 2026-02-16. Predicting the success of new crypto-tokens: the Pump.fun case. https://arxiv.org/abs/2602.14860

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