BIS Study Flags Key Blind Spot in Bitcoin On-Chain Transfer Data

Estimates of how much economic activity flows through crypto networks can diverge dramatically depending on how analysts measure onchain activity. A new study from researchers at the Bank for International Settlements (BIS) finds that calculations of Bitcoin transfer values can vary by as much as six times when different transaction-measurement methods are used.

The BIS researchers argue that the problem is not limited to Bitcoin. The same measurement challenges appear across the wider crypto ecosystem—including stablecoin activity and even conventional approaches to calculating market capitalization. Their conclusion: onchain indicators should be treated as imperfect, “noisy approximations,” not direct gauges of real-world economic activity.

Key takeaways

• Bitcoin onchain transfer-value estimates can differ by up to sixfold based on measurement choices, including how change outputs are handled.
• BIS finds conventional Bitcoin market capitalization figures have, at times, been up to four times higher than “realized” capitalization based on the last time a coin moved.
• Across large-scale data spanning Bitcoin, Ethereum, and Tron, the study shows similar measurement pitfalls in other parts of the market.
• On Ethereum, the abundance of smart contracts creates categorization gaps that complicate interpretation of activity, including stablecoins.
• Some analytics efforts—such as Visa’s Onchain Analytics dashboard—attempt to adjust raw stablecoin volumes to remove distortions from non-economic activity.

Why Bitcoin “transfer value” can change six times

The BIS findings focus on onchain transfer values rather than exchange trading volumes. According to the study, the gap between different estimates reflects differences in transaction measurement methods—most notably how the analysis treats outputs that send funds back to the original sender.

Bitcoin transactions are structured in a way that often includes “change” outputs. When a user spends Bitcoin, the network may return any unspent portion back to the spender as change. Some measurement approaches count that as an additional output, even though it does not represent value transferred to another counterparty.

The BIS researchers caution that metrics that are commonly used to infer activity—such as transaction volumes, market capitalization, and total value locked—can appear more precise than the underlying data actually supports. As the study puts it, those metrics can suggest accuracy that is “not supported by the nature of the underlying data.”

Market capitalization: a conventional figure can diverge

The BIS study does not stop at onchain transfer estimates. It also highlights inconsistencies in how market capitalization is typically calculated for Bitcoin.

The researchers report that the conventional market cap measure has at times been as much as four times higher than “realized capitalization,” a metric that values each coin at the price when it last moved. In practical terms, the difference underscores a broader issue: different ways of interpreting blockchain movement can generate materially different economic readouts.

This matters for investors and analysts who use onchain-derived figures to gauge adoption, liquidity, or sentiment. When measurement methodology can swing the headline number by multiples, comparisons across time periods—or across dashboards with differing definitions—require careful scrutiny.

Ethereum and stablecoins: categorization gaps and mixed use cases

The BIS researchers identify additional challenges on Ethereum, where smart contract activity multiplies the ways tokens can be held or moved. In the study’s dataset, researchers examined roughly 67.5 million active contracts and found that about 54 million could not be categorized using the study’s classification approach.

Stablecoins introduce a further layer of complexity because the same token can serve different roles across chains. The BIS researchers note that USDT on Ethereum was more closely associated with DeFi activity, while USDT on Tron was more tied to payment-like and store-of-value uses.

The study also describes stark differences in where stablecoins sit—particularly in smart contract holdings. The share of USDT held by smart contracts on Ethereum exceeded 20% in 2022, compared with around 1% on Tron. Because these holdings reflect different use cases, the BIS researchers warn that aggregating stablecoin activity across blockchains can conflate distinct kinds of economic behavior and obscure how stablecoins are actually being used.

Ultimately, the BIS team frames the broader takeaway as a limitation of data interpretation: onchain indicators should be handled as “noisy approximations rather than direct measures of economic activity.”

Filtering raw data: Visa’s adjusted stablecoin volumes

While the BIS study emphasizes the risks of treating raw onchain measures as straightforward economic signals, it also notes that some analytics providers attempt to separate “economic activity” from activity that may be distorted by mechanics or automation.

Visa’s Onchain Analytics dashboard—powered by data from Allium Labs—presents both total and adjusted stablecoin transaction volumes. Visa states that its adjusted methodology is designed to reduce distortions stemming from activity such as high-frequency trading, bots, bridge routing, and internal exchange operations.

On the dashboard, Visa currently shows $6.4 trillion in total stablecoin transaction volume across the networks it tracks over the past 30 days, alongside $313.1 billion in adjusted volume. While the BIS study itself does not validate any specific proprietary adjustment approach, the contrast illustrates the central issue it raises: definitions and filtering choices can move the headline number by a wide margin.

For readers using dashboards to benchmark stablecoin adoption, the implication is straightforward: “total” and “adjusted” are not interchangeable, and the rationale behind adjustments becomes part of the metric’s credibility.

What to watch next

The BIS study suggests that as onchain analytics matures, transparency about measurement definitions—and explicit handling of transaction structure, smart-contract categorization, and non-economic activity—will be essential. Investors and builders should treat widely cited onchain metrics as starting points, not definitive proof of underlying economic demand, and should watch for clearer methodologies that better align onchain observations with real-world usage.