Crypto Value, Factor Pricing, and Market Segmentation
基于最大规模加密资产数据集，发现活跃地址与市值比驱动的价值效应，构建四因子模型解释回报差异，并首次按经济功能分类加密货币，揭示代币类别间的市场分割及其对投资和监管的启示。
In the largest data set of crypto assets to date, we uncover a significant value effect based on the active-addresses-to-market-cap ratio. The corresponding novel value factor, together with the crypto market, size, and momentum factors adapted to our sample, forms a four-factor model that explains the cross-sectional return variations better than existing benchmarks. We show that the crypto value premium is plausibly driven by compensation for on-chain activity risk. We also provide the first comprehensive classification of major cryptocurrencies based on their economic functionality. Applying methodologies from international asset pricing, we document significant market segmentation across the token categories, with nonmonotone dynamics and implications for crypto investment strategy and regulation. This paper was accepted by Christoph Loch, finance. Conflict of Interest Statement: L. W. Cong serves as a senior economist and senior economic advisor to Chainlink Labs; G. A. Karolyi serves as an ad hoc consultant to Avantis Investors. Funding: K. Tang’s work was supported by the National Natural Science Foundation of China [Grants 72192802, 72342008]. W. Zhao’s work was supported by the National Natural Science Foundation of China [Grant 72503258], the China Postdoctoral Science Foundation [Grant 2025M773717], and the Innovation and Talent Base for Digital Technology and Finance [Grant B21038]. L. W. Cong’s work was supported by the Ripple University Blockchain Research Initiative. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05875 .