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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.14860 |
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| _version_ | 1866917276544925696 |
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| author | Marino, Giulio Naviglio, Manuel Tarantelli, Francesco Lillo, Fabrizio |
| author_facet | Marino, Giulio Naviglio, Manuel Tarantelli, Francesco Lillo, Fabrizio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14860 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Predicting the success of new crypto-tokens: the Pump.fun case Marino, Giulio Naviglio, Manuel Tarantelli, Francesco Lillo, Fabrizio Statistical Finance 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. |
| title | Predicting the success of new crypto-tokens: the Pump.fun case |
| topic | Statistical Finance |
| url | https://arxiv.org/abs/2602.14860 |