# Crypto Wash Trading and Real Liquidity: Fake-Volume Estimates Run From 51% to 95% Published estimates put fake or non-economic bitcoin volume at 51% to 95%, and above 70% on unregulated venues. Why the studies disagree, why depth and slippage judge a venue better than volume (Binance: 64.3% of volume, 30.7% of depth, per Kaiko), and what to check yourself. Canonical URL: https://vultax.com/research/crypto-wash-trading-what-the-research-shows Author: [Vultax Research](https://vultax.com/editorial-policy) Published: 2026-09-02 Last public revision: 2026-09-16 Published research snapshots and calculations; these exports do not contain current quotes or live account data. Observation windows and populations are stated in the source captions and methodology. Missing metadata is unknown, not zero. This article summarises third-party research and public enforcement records and describes Vultax market-quality screens. Screens produce probabilistic risk readings from public market data; they are not allegations of wrongdoing against any exchange, venue or account holder, and they do not establish beneficial ownership or intent. Charges cited are allegations unless and until proven. Nothing here is financial advice or a recommendation to trade. ## Key figures - Published fake-volume estimates: 51%–95% — Share of reported bitcoin volume judged fake or non-economic: Forbes 2022, 157 exchanges, to Bitwise 2019, an 83-exchange sample - Unregulated venues: >70% — Share of reported volume estimated as wash trading by Cong, Li, Tang and Yang across 29 centralised exchanges - Binance volume share vs depth share: 64.3% vs 30.7% — Share of global trade volume against share of global market depth, in Kaiko's 2023 measurement - Charged, March 2026: 10 — Foreign nationals tied to four market-making firms, after an undercover FBI operation that traded a bureau-created token ## The short answer There is no agreed figure for how much reported crypto volume is real. Published estimates of the fake or non-economic share of bitcoin volume run from about 51% (Forbes, 2022, 157 exchanges) to roughly 95% (Bitwise, 2019, an 83-exchange sample), and an academic study of 29 centralised exchanges found wash trading averaged over 70% of reported volume on the unregulated ones while regulated venues behaved like conventional markets. That regulated-versus-unregulated gap is the most consistent finding across all of them. Volume is the easiest number to inflate, because it can be printed without consuming liquidity; depth cannot be faked without capital actually at risk in the book. In Kaiko's measurement Binance accounted for 64.3% of global trade volume but 30.7% of global market depth. So judge a venue by what its book will absorb: spread, depth near the mid, how fast the book refills, and the slippage you actually pay at your size. ## Three studies, three very different numbers The three most cited results are worth stating together, because seeing them side by side is more informative than any one of them alone. In 2019 [Bitwise Asset Management filed research with the U.S. Securities and Exchange Commission](https://www.sec.gov/comments/sr-nysearca-2019-01/srnysearca201901-5164833-183434.pdf) concluding that roughly 95% of CoinMarketCap-reported bitcoin spot volume, across an 83-exchange sample, was fake or non-economic, and that only ten venues showed volume it considered entirely real. In 2022 [Forbes ran its own assessment](https://www.forbes.com/sites/javierpaz/2022/08/26/more-than-half-of-all-bitcoin-trades-are-fake/) across 157 exchanges and put the figure at about 51%. In the academic literature, [Cong, Li, Tang and Yang](https://www.nber.org/system/files/working_papers/w30783/w30783.pdf) examined 29 centralised exchanges and found that wash trading averaged over 70% of reported volume on the unregulated ones, while the regulated venues in their sample behaved like conventional financial markets. A 44-percentage-point spread between published estimates is not a rounding difference. It is the signature of three teams measuring different exchange samples, over different periods, with different definitions of what counts as fake — and because the samples overlap only partly, the headline figures are not directly comparable. ## Why the estimates disagree The disagreement is mostly definitional. “Wash trading” in the strict sense means trades where beneficial ownership does not change — an account trading against itself, or two accounts under common control trading with each other. “Non-economic volume” is a broader category that also captures incentivised trading, volume generated by exchange-run market-making programmes, and fee rebates structured so that trading at a loss is still profitable. The narrower the definition, the smaller the headline number. The Forbes figure and the Bitwise figure are not two measurements of the same quantity that happen to differ; they are partly measurements of different quantities. Sample construction matters just as much. An estimate built from the long tail of small venues will find more fabricated volume than one restricted to the largest exchanges, because the incentive to fabricate is strongest where listing rank and aggregator visibility are the primary source of new users. ## How researchers actually detect it None of these studies had access to exchange account records, so none could observe beneficial ownership directly. What they could test is whether the statistical shape of reported trades matches the shape genuine trading produces. Real order flow has well-documented regularities in its trade sizes, and fabricated volume, especially when generated programmatically, tends to break at least one: sizes are too uniform, too round, or too evenly distributed across the digit range. The method is powerful and inherently probabilistic. It identifies volume whose statistical fingerprint is inconsistent with organic trading; it does not identify who generated that volume or establish intent, and every serious paper in this literature says so. - First-significant-digit tests — genuine trade sizes follow a known digit distribution; generated sizes often do not - Trade-size roundness — an unusually high share of perfectly round sizes is a common artefact of automated wash trading - Tail-distribution tests — the largest trades in a real market follow a recognisable power law ## What changed since the headline studies Three things. The regulatory perimeter widened, enforcement moved from statistics to stings, and detection moved from distributional tests to machine learning on trade-level features. The strongest finding in the academic work is not the 70% figure itself but the gap between regulated and unregulated venues in the same sample and period: where a venue carries market-surveillance obligations, the anomalies largely disappear. That perimeter has widened. In the European Union the [Markets in Crypto-Assets Regulation](https://eur-lex.europa.eu/eli/reg/2023/1114/oj) prohibits market manipulation in crypto-assets and requires licensed venues to run surveillance for it, with those provisions applying since December 2024. Enforcement has become direct. In October 2024 the U.S. Department of Justice [charged eighteen individuals and entities](https://www.justice.gov/usao-ma/pr/eighteen-individuals-and-entities-charged-international-operation-targeting-widespread) after the FBI created a token of its own and market-making firms agreed to generate volume for it. In March 2026 it [charged ten foreign nationals](https://www.justice.gov/usao-ndca/pr/ten-foreign-nationals-charged-international-operation-targeting-cryptocurrency-market) tied to four market-making firms — Gotbit, Vortex, Antier and Contrarian — after a second undercover operation by the FBI and IRS Criminal Investigation, alleging wash trading of bureau-created tokens to inflate their volume and price. These are allegations until proven; what they establish regardless of verdict is that volume-for-hire is sold openly enough for an undercover buyer to purchase it. [Recent detection work](https://www.sciencedirect.com/science/article/abs/pii/S0048733326000995) applies tree-based and deep-learning models to trade-level features. On NFT marketplaces it flagged around 38% of trades and 60% of traded value as wash trades, with wide variation between platforms. The technique generalises; those percentages do not transfer to spot exchanges. ## Prediction markets are not exempt The largest new datapoint since the studies above is not on a crypto exchange. In November 2025 Columbia researchers, matching Polygon wallets that traded mostly with the same small set of counterparties, flagged about a quarter of Polymarket's volume over three years as wash trading, peaking near 60% of monthly volume in December 2024, with sports markets carrying the highest share and many flagged wallets making no profit at all, consistent with farming a future airdrop or a ranking rather than a return. The method reads counterparty structure rather than trade-size statistics, which is possible because a prediction market publishes every fill on-chain. Regulators moved in the same direction. The CFTC's Enforcement Division issued a prediction-markets advisory on 25 February 2026 that names wash sales and pre-arranged trading alongside insider trading, manipulation and disruptive trading as conduct it polices on any designated contract market, and reminds venues of their own duty to keep audit trails and surveil. For the crypto venues in this article nothing comparable applies to offshore spot, which is why the public-data checks below remain the trader's only instrument. ## Volume and liquidity measure different things Volume is a count of what has already traded. Liquidity is a statement about what could trade now without moving the price. A venue can report enormous volume and hold almost nothing in its book; it can also hold deep resting liquidity while reporting modest turnover. Traders default to volume because aggregators display it and exchanges promote it, and it is the number the estimates above show to be easiest to inflate. The gap between the two is not hypothetical. In [Kaiko's measurement of global crypto market structure](https://research.kaiko.com/insights/the-crypto-liquidity-concentration-report), Binance accounted for 64.3% of global trade volume but 30.7% of global market depth — a factor-of-two divergence between what the venue processed and what it could absorb. Across the top eight venues the figures were closer, at 91.7% of depth against 89.5% of volume, which tells you the divergence is concentrated rather than uniform. ## Liquidity is more concentrated than volume, and getting more so The structural finding in the liquidity research is concentration. Kaiko reports that liquidity has become more concentrated over time, with market makers deploying capital where organic activity already is, and quoting in a tighter range when they do. For ETH specifically, 72% of liquidity sits on five exchanges. This is self-reinforcing. Market makers go where flow is, flow goes where execution is cheapest, and execution is cheapest where market makers are, so the effective venue set for any asset is much smaller than the list of exchanges that quote it. A venue outside that core reporting competitive volume on a major pair is making a claim the depth data does not generally support. ## The four measurements that describe real liquidity Liquidity is not one number. Four measurements together give a usable picture, and each answers a different question about the same book. - Quoted spread — the distance between best bid and best offer. Answers: what does an instant round trip cost at minimum size? - Market depth at a band — cumulative resting size within a fixed distance of mid, commonly measured at 0.1%, 1% and 2%. Answers: how much can I move before I move the price? - Slippage or effective spread — the difference between the price you saw and the volume-weighted price you got. Answers: what did it actually cost? - Depth resilience — how quickly the book refills after being consumed. Answers: can I do that again in a minute? ## Why quoted spread alone is misleading Quoted spread is the most visible liquidity metric and the easiest to game. A single small order at the touch produces a tight spread while saying nothing about the size behind it. Two venues can show identical spreads and differ by an order of magnitude in what they will absorb. The industry consensus is that [execution quality is best measured by realised costs](https://www.coinapi.io/blog/execution-quality-in-crypto): effective spread, implementation shortfall and realised slippage, segmented by time of day and around event windows, because fill quality degrades exactly when correlated flow arrives. The version for a trader without an execution-analytics stack is a slippage log. Record the mid at decision time and the volume-weighted price you received on every fill, per venue and per size band. Volume tells you what an exchange says happened and depth what its book will absorb; slippage is what you actually paid, and it is the only one of the three measured at your size. ## What a trader can check without a data vendor Most of the useful signal is available from public exchange data if you know what to compare. The single most informative check is the relationship between reported volume and observable order-book depth. Genuine volume is expensive to produce, because it consumes real liquidity; fabricated volume is cheap, because it does not. A venue reporting volume far out of proportion to the depth resting in its book is describing a market that would not absorb the trades it claims to process. The second check is behavioural: genuine volume spikes on catalysts and thins overnight, so volume that is flat across sessions or ignores a large price move describes a market with no participants in it. Any venue comparison needs the same pair, the same moment and the same size. A single snapshot captures whichever venue had a market maker quoting at that instant, and a venue with a strong Asian-hours book and a thin European one is a different venue depending on when you trade. - Compare volume to resting depth — a large ratio means volume that the visible book could not have supported - Compare across venues on the same pair at the same moment — one venue diverging from the rest is more informative than any absolute number - Check the intraday shape — genuine volume has a session profile; generated volume often does not - Check whether spreads widen when volume spikes — in a real market under stress, they usually do - Repeat across sessions and weekdays, and report the distribution rather than a single reading - Weight by the size you actually trade, not by the size that makes a venue look best ## What Vultax measures, and what it does not Vultax runs market-quality and liquidity-health screens across the venues it connects to and surfaces them in Vi IQ, its 0-100 pre-trade read, as two of six domains alongside order flow, arbitrage potential, volatility and news sentiment. The domain scores are shown next to the headline number so you can see why, and how each is built is documented in the [methodology](https://vultax.com/methodology). The result is a probabilistic risk reading derived from order-book and trade behaviour, not an identification of a participant: Vultax has no access to exchange account records, and no screen built on public market data can establish beneficial ownership. Its use is comparative and defensive. A degraded reading when you are about to size a position is a reason to check depth and spread first, or route elsewhere, not a verdict on the venue. A composite is only as good as the book data behind it, and [coverage and feed latency by venue](https://vultax.com/research/crypto-exchange-feed-latency-september-2026) are published. ## How to read the live figures The strip at the top of this page is not a screenshot of the day this was written. Every ten minutes Vultax re-reads CoinGecko's per-pair anomaly and stale flags on six venues, its exchange trust scores, and the reported volume of the top fifteen venues and rewrites the figures; the Vi IQ beneath them scores the same six domains the terminal scores for a pair, computed for BTC on the major spot venues, with any domain that cannot be computed shown as unavailable rather than filled in. Read the numbers as a live check on the argument above, and the revision notes at the end for what has changed since publication. The first two figures are the count of pairs CoinGecko currently flags as anomalous across six venues and the number of pairs it checked; the third is the mean trust score it assigns those venues; the fourth is the bitcoin volume the top fifteen venues report for the day. A flag is not proof of wash trading, and a trust score leans on reported volume, but a venue whose flagged share rises while its reported volume holds is showing the pattern the studies describe. ## Context from elsewhere Reported volume keeps growing while measured liquidity thins. CoinGecko counts close to $80 trillion of centralised-exchange volume across spot and perpetuals in 2025. In January 2026 Glassnode measured spot volume at its lowest since November 2023 and CryptoQuant's exchange whale ratio stood at a ten-month high, a combination analysts read as large holders using thin books as exit liquidity. By July 2026 CoinDesk was describing a survival crisis for smaller venues as day traders disappeared. None of that shows in a volume ranking. It shows in depth, in spread under load, and in how often a venue's pairs are flagged as anomalous. The trust score CoinGecko assigns an exchange still leans on reported volume; the anomaly and stale flags on individual pairs are the part of its data that does not, which is why the live strip leads with them. ## Method and limits Every published estimate of crypto wash trading is a modelled inference from market data, not a count. The honest summary: a material share of reported volume on unregulated venues does not behave like organic trading, credible estimates of that share run from roughly half to nearly all depending on definition and sample, and the regulated-versus-unregulated gap is the most consistent finding across all of them. Anyone quoting a single precise figure without naming the study, the sample and the definition behind it is overstating what the research supports. Third-party liquidity figures, including Kaiko's, describe the venues and windows those studies covered and may not hold today. Vultax readings are bounded by feed coverage on each venue. To compare what venues can absorb rather than what they report, the BTC venue desk in the Vultax terminal shows each connected exchange's book with depth, liquidity and large trades. ## Questions ### How much crypto trading volume is fake? Published estimates of fake or non-economic bitcoin volume run from about 51% (Forbes, 2022) to roughly 95% (Bitwise, 2019), and academic work found wash trading averaged over 70% of reported volume on unregulated venues. It depends on the venue, the year and the definition; regulated venues sit at the low end. ### What is the difference between exchange liquidity and trading volume? Volume is a count of what traded, reported by the venue. Liquidity is what an order can use now: resting size near the mid, the spread, and how far a given order moves the price. In Kaiko's measurement Binance carried 64.3% of global trade volume but 30.7% of global market depth. ### How can I tell if a venue's volume is real? Compare its reported volume with the size resting within 1% and 2% of mid, and watch spread and depth during a fast move. Check whether a data aggregator flags its pairs as anomalous or stale, and look for volume that clusters at round sizes or ignores the daily session. ### Does wash trading happen on Polymarket? A 2025 Columbia study flagged about 25% of Polymarket's volume over three years as wash trading, peaking near 60% in December 2024, using on-chain counterparty patterns. Sports markets carried the highest share. ### Is wash trading illegal? On U.S.-regulated venues, yes: it is prohibited under the Commodity Exchange Act, and the CFTC's February 2026 advisory applies that to prediction markets explicitly. On offshore spot venues enforcement depends on the venue's own rules and jurisdiction. ## Revision notes - 2026-09-07: Live figures and a Vi IQ for this subject now refresh every ten minutes on this page from outside sources and Vultax's own tables; a context section, the questions below and these revision notes were added. - 2026-09-07: Added the November 2025 Columbia study of Polymarket and the CFTC's February 2026 advisory; the live strip now reports CoinGecko's anomaly flags and trust scores for six venues. - 2026-09-16: Now also covers what measures real liquidity instead of reported volume: spread, depth near the mid, slippage at your size, and how concentrated depth is across venues. ## Sources and methodology - [Cong, Li, Tang & Yang — Crypto Wash Trading (NBER Working Paper 30783)](https://www.nber.org/system/files/working_papers/w30783/w30783.pdf) — 29 centralised exchanges; wash trading averaged over 70% of reported volume on unregulated venues. Later published in Management Science. - [Crypto Wash Trading — preprint (arXiv 2108.10984)](https://arxiv.org/pdf/2108.10984) — Open-access version of the same study, including the statistical detection methodology - [Bitwise Asset Management presentation to the SEC (2019)](https://www.sec.gov/comments/sr-nysearca-2019-01/srnysearca201901-5164833-183434.pdf) — 83-exchange sample; concluded roughly 95% of reported bitcoin spot volume was fake or non-economic - [Forbes — More Than Half Of All Bitcoin Trades Are Fake (2022)](https://www.forbes.com/sites/javierpaz/2022/08/26/more-than-half-of-all-bitcoin-trades-are-fake/) — 157-exchange assessment putting fake or non-economic volume at approximately 51% - [U.S. Department of Justice — Ten foreign nationals charged in an international operation targeting cryptocurrency market manipulation (N.D. Cal., 2026)](https://www.justice.gov/usao-ndca/pr/ten-foreign-nationals-charged-international-operation-targeting-cryptocurrency-market) — Undercover FBI and IRS-CI operation using bureau-created tokens; defendants tied to Gotbit, Vortex, Antier and Contrarian alleged to have wash traded them - [U.S. Department of Justice — Eighteen individuals and entities charged in an international operation targeting fraud and manipulation in the cryptocurrency markets (D. Mass., 2024)](https://www.justice.gov/usao-ma/pr/eighteen-individuals-and-entities-charged-international-operation-targeting-widespread) — The earlier undercover operation built around an FBI-created token - [Regulation (EU) 2023/1114 on markets in crypto-assets (MiCA)](https://eur-lex.europa.eu/eli/reg/2023/1114/oj) — Prohibits market manipulation in crypto-assets and requires trading platforms to detect and prevent it; those provisions apply from 30 December 2024 - [Detecting crypto wash trades via machine learning (Research Policy, 2026)](https://www.sciencedirect.com/science/article/abs/pii/S0048733326000995) — Trade-level ML detection using on-chain data; flagged ~38% of trades and ~60% of traded value in NFT marketplaces - [Nasdaq — Crypto Wash Trading: Detection Challenges and Prevention](https://www.nasdaq.com/articles/fintech/crypto-wash-trading-why-its-still-flying-under-the-radar-and-what-institutions-can-do-about-it) — Industry overview of why pseudonymity and fragmentation make detection difficult - [Vultax methodology](https://vultax.com/methodology) — How Vultax market-quality and liquidity-health screens and the Vi IQ composite are constructed - [Gizmodo — Study finds around a quarter of Polymarket trades are fake (Columbia, Nov 2025)](https://gizmodo.com/study-finds-around-a-quarter-of-polymarket-trades-are-fake-2000683231) — Columbia researchers flagged about 25% of Polymarket volume as wash trading over three years, peaking near 60% in December 2024, by matching wallets that traded mostly with the same counterparties. - [CoinDesk — Polymarket's trading volume may be 25% fake, Columbia study finds](https://www.coindesk.com/markets/2025/11/07/polymarket-s-trading-volume-may-be-25-fake-columbia-study-finds) — Sports markets carried the highest flagged share (about 45% of historical volume); election markets about 17%. - [CFTC — Enforcement Division issues Prediction Markets Advisory (25 Feb 2026)](https://www.cftc.gov/PressRoom/PressReleases/9185-26) — Names insider trading, fraud and manipulation, wash sales and pre-arranged trading, and disruptive trading as conduct the Commission polices on any designated contract market, and reminds venues of their duty to keep audit trails and surveil. - [CoinGecko — CEX & DEX Trading Activity Report 2026](https://www.coingecko.com/research/publications/cex-dex-trading-activity-report-2026) — Centralised exchanges processed close to $80 trillion across spot and perpetual markets in 2025, by CoinGecko's count of reported volume. - [Kaiko — The Crypto Liquidity Concentration Report](https://research.kaiko.com/insights/the-crypto-liquidity-concentration-report) — Top 8 venues held 91.7% of global market depth against 89.5% of volume; Binance 30.7% of depth against 64.3% of volume - [Kaiko — 72% of ETH Liquidity is Concentrated on 5 Exchanges](https://www.kaiko.com/resources/72-of-eth-liquidity-is-concentrated-on-5-exchanges) — Asset-level concentration measurement - [CoinAPI — Execution Quality in Crypto: Measuring Slippage and Best Execution](https://www.coinapi.io/blog/execution-quality-in-crypto) — Effective spread and implementation shortfall over quoted spread; segmenting realised slippage by session and event window - [CoinMarketCap — Liquidity Score methodology](https://support.coinmarketcap.com/hc/en-us/articles/360043836931-Liquidity-Score-Market-Pair-Exchange) — How a widely referenced public liquidity score is constructed at market-pair and exchange level - [TokenInsight — Crypto Exchange Liquidity Report](https://tokeninsight.com/en/research/reports/crypto-exchange-liquidity-report-jul-2026) — Order-book depth, slippage and bid-ask spread compared across nine major exchanges - [Yahoo Finance — Bitcoin whales accelerate exchange activity in early 2026 amid fragile liquidity](https://finance.yahoo.com/news/bitcoin-whales-accelerate-exchange-activity-123727209.html) — CryptoQuant's exchange whale ratio (top-10 inflows over all inflows) at a ten-month high; Glassnode spot volume at its lowest since November 2023. - [CoinDesk — Crypto exchanges face a survival crisis as day traders disappear (28 Jul 2026)](https://www.coindesk.com/business/2026/07/28/bitmex-and-bitmart-may-be-first-casualties-of-crypto-trading-slump) — A spot-volume slump that thins books before it shows in any headline volume figure; BitMEX announced a September shutdown. Changing market context is available on the [article page](https://vultax.com/research/crypto-wash-trading-what-the-research-shows). It is separate from the published study exported here.