NFT markets are venues where non-fungible tokens, unique assets recorded on a blockchain, change hands. They look like ordinary financial markets from a distance: listings, bids, volume charts, price histories. Up close they are structurally strange. Every unit is one of a kind, every trade is publicly visible forever, settlement is bilateral rather than through a central order book, and for stretches of their history the headline activity numbers were substantially fictional.
That combination, radical transparency plus unreliable statistics, is what makes NFT markets worth studying. It is the setting for my paper The Economics of Wash Trading, which uses them as a laboratory for questions that are hard to answer in equities: when activity can be manufactured for free, who manufactures it, and does it work?
How the plumbing works
A typical NFT trade is a bilateral settlement executed by a marketplace contract: a seller signs a listing at a price, a buyer accepts it, and the token and payment move in one atomic transaction. Around that primitive, marketplaces build the familiar furniture:
- Listings and offers instead of a continuous order book. Each token has its own tiny market; the collection floor price, the cheapest live listing, becomes the de facto reference price.
- Rankings and trending pages sorted by volume, which route attention, and therefore demand, toward whatever prints the biggest numbers.
- Fee and reward schedules: creator royalties, marketplace fees, and in some cases token rewards paid out in proportion to trading volume. The last of these turned out to be the load-bearing detail.
Price formation with thin books
In liquid equities, thousands of identical shares trade per minute and no single print defines the price. In an NFT collection, a token might trade once a month. Reference prices hang off a handful of observations: the floor, the last sale, a recent sweep. Thin markets amplify small flows, which cuts both ways. Genuine enthusiasm moves prices fast, and so does a manipulator with two wallets. When a single self-trade can set a collection's reference price, painting the tape costs a gas fee.
This is why volume statistics matter so much more in NFT markets than elsewhere: with little other information, participants lean on activity as the demand signal. And it is why wash trading, the manufacture of exactly that signal, is the characteristic manipulation of the asset class.
The efficiency question
Are NFT markets efficient? The transparency argues yes: every trade, wallet, and transfer is public, so information should travel. The structure argues no: unique assets, thin liquidity, retail-heavy participation, and statistics that reward gaming. The empirical answer from The Economics of Wash Trading is closer to the second reading, with a twist. Wash trading volume, however large, showed no significant relationship with genuine future volume. The market was inefficient enough to be flooded with fake activity, but participants were not, in aggregate, fooled into trading behind it. The fake volume found its payoff elsewhere: in token incentives that paid for volume directly.
Reading NFT market data without being lied to
A short field guide, distilled from the research:
- Treat volume as a claim, not a fact. Ask what share survives once self-financed wallet clusters are removed. The methods are on the detection page.
- Check what the venue pays for. If a marketplace rewards volume with its own token, assume the volume statistics are contaminated until shown otherwise.
- Prefer breadth to depth. The number of distinct, independently funded buyers says more about demand than the dollar total any of them printed.
- Watch prices, not just prints. In the data behind the paper, average prices and genuine volumes moved on their own logic, largely indifferent to the wash layer stacked on top.
The scale of the fake layer, and who profited from it, is quantified on the NFT wash trading page. Whether any of it was legal is covered on Is wash trading illegal?