A quantitative framework for crypto asset pricing

Abstract

We estimate a multifactor asset pricing model for cryptocurrencies by ex tending the Fama and French (1992, 1993) framework with on-chain indicators derived from blockchain data. Using weekly observations from 2018 to 2025, we quantify the explanatory power of traditional factors (market and size) alongside metrics such as active addresses (AA), a proxy for total number of agents in the market, and the network-to-transfer value (NVT), a proxy of market capitalisation weighted by the transfer value. Results show that the inclusion of size and on-chain factors improves model fit relative to a baseline CAPM. AA and NVT emerge as key return drivers and this is confirmed by a SHAP analysis. Market-neutral strategies based on these factors outper form benchmarks in risk-adjusted terms, highlighting the economic relevance of blockchain-native information in cryptoasset valuation