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

Battle Count: 58

Highly relevant for crypto-native quantitative trading strategies, particularly in the meme token and launchpad segment. The paper provides actionable signals (trading velocity, bot share, creator identity) that could inform entry/exit decisions in bonding-curve markets. The economic breakeven framework directly addresses profitability. However, the extreme low base rate (0.63%) and the speculative nature of most tokens limit applicability to traditional quantitative trading. The pump-and-dump detection methodology is directly applicable to market surveillance and alpha generation in crypto markets. The findings on liquidity velocity as the strongest predictor are practically actionable.

The core methodology (conditional probability estimation, non-parametric binned estimates) is straightforward. However, the data pipeline requires: (1) parsing Solana blockchain transaction logs, (2) decoding Pump.fun and PumpSwap program events, (3) reconstructing time-resolved bonding curve states, (4) implementing the Shewhart control chart with median-MAD estimation, (5) constructing time-consistent conditioning variables, and (6) handling the extreme class imbalance. Solana-specific blockchain expertise and significant computational resources for processing millions of transactions are needed. The analytical framework itself is relatively simple but data engineering is substantial.

The paper uses fully on-chain data from Solana blockchain, which is publicly accessible. The methodology is transparent with explicit formulas for bonding curve mechanics, graduation probability estimation, and dump detection. However, no code repository is mentioned. The dataset construction from blockchain logs is described in detail, enabling replication. The one-month sample period (September 2025) is specified. The isBot classification method based on transaction logs is described but may require Solana-specific expertise to replicate.