Aggregate Confusion in Crypto Market Data

We present one of the first systematic audits of cryptocurrency market data quality across leading vendors. We document pervasive mislabeling, identifier instability, and large cross-provider discrepancies in prices, market caps, and volumes. To address these issues, we develop an aggregation method that yields asymptotically correct data by autonomously identifying and filtering unreliable observations. Using this framework, we construct an index to measure data quality over time and a grading system to benchmark providers. Our findings show that data inconsistencies can materially distort empirical research and investment analysis. They highlight possible oversight gaps in the market for crypto data. This paper was accepted by Will Cong for the Special Issue on Digital Finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2025.00611 .