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

TL;DR

This paper analyzes the factors influencing the success of new crypto-tokens launched on Pump.fun, using predictive models to understand how initial liquidity and launch dynamics affect token graduation to the on-chain market.

Contribution

It introduces predictive models for token success based on bonding curve mechanics and launch variables, offering new insights into early-stage market behavior and launch dynamics.

Findings

Bonding curve initial liquidity predicts token success.

Structural and behavioral variables improve prediction accuracy.

Insights into speculative and manipulative behaviors in token launches.

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.