Why standard Bitcoin transfer figures are off by up to six times, according to the BIS

Researchers at the Bank for International Settlements (BIS) have shown that estimates of Bitcoin transfer value can vary dramatically depending on the measurement method used. Their working paper, released on September 15, finds that different approaches can produce totals that differ by as much as a factor of six.

Why the numbers diverge

The divergence stems from three structural sources:

• Transaction aggregation: How individual movements on the blockchain are grouped and interpreted.
• Smart‑contract programmability: The presence of contracts that can move funds in complex ways.
• Cross‑chain comparisons: Differences in how activity is classified across different blockchains.

These choices are invisible in headline totals, meaning that the same raw ledger data can support very different economic narratives.

Beyond Bitcoin: contracts and stablecoins

The paper extends the analysis to smart contracts and stablecoins. It classifies roughly 13 million active contracts, including about 1.4 million distinct tokens, and notes that rapid contract proliferation and extensive token issuance make it harder to isolate economically meaningful activity.

Stablecoin activity also varies by network. On Ethereum, stablecoins are largely tied to smart‑contract interactions, while on Tron they are more often held outside contracts, suggesting transactional or store‑of‑value uses. This variation blurs cross‑chain activity rankings when raw transaction counts are treated as equivalent.

Implications for on‑chain metrics

The authors argue that on‑chain indicators should be treated as noisy approximations rather than precise measures of economic activity. They recommend granular, data‑bounded estimates that make methodological assumptions explicit, allowing analysts to connect ledger events to economic meaning more reliably.

Practical takeaway

When using on‑chain figures—whether transfer value, active contracts, or stablecoin holdings—the methodology behind the numbers is crucial. Transparent counting rules enable more meaningful comparisons across networks and time periods.