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

Author

Listed:
  • Giulio Marino
  • Manuel Naviglio
  • Francesco Tarantelli
  • Fabrizio Lillo

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.

Suggested Citation

  • Giulio Marino & Manuel Naviglio & Francesco Tarantelli & Fabrizio Lillo, 2026. "Predicting the success of new crypto-tokens: the Pump.fun case," Papers 2602.14860, arXiv.org.
    • Handle: RePEc:arx:papers:2602.14860
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    File URL: http://arxiv.org/pdf/2602.14860
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