Open-world tokenized equities: a dataset linking blockchains and equity markets

Tokenized equities are an emerging class of real-world assets that connect public blockchains with traditional equity markets. Yet this rapidly growing market remains underexplored. Existing AI-for-finance datasets model prices, text, and inter-stock relations, while blockchain datasets capture wallets and transactions; neither jointly observes public on-chain activity and the corresponding listed-equity information. We introduce, to our knowledge, the first large-scale ticker-aligned dataset bridging these domains, covering 890 tokenized equities, 249.5M transfers, and 31.3M swaps, together with exchange prices, news, and fundamentals. This alignment uncovers new empirical findings. Tokenized equities remain active when U.S. exchanges are closed, and fresh, liquid on-chain quotes can predict subsequent exchange price movements. A small set of liquid tokenized assets also carries information about the broader equity market, while account-level co-holding is more strongly associated with shared news attention than with return correlation. These findings motivate two complementary learning tasks enabled by the linked dataset: dark-window equity forecasting, which augments off-chain market data with evolving on-chain signals to predict the direction of the next opening price, and open-world next-asset prediction, which predicts participation in newly tokenized equities from account histories and asset information. Together, the dataset, findings, and tasks introduce a new learning setting spanning on-chain–equity forecasting and temporal asset recommendation.