What is NFT Wash Trading?
Wash trading is a form of market manipulation in which market participants attempt to influence prices through repeated buying and selling, creating the appearance of a large amount of trading activity. On the one hand, the purpose of wash trading may be to create the illusion of oversupply of the project, and on the other hand, it may be to obtain the activity rewards provided by the platform.
Wash Trading Status
Traditional securities and futures markets prohibit wash trading, but there are still a large number of Wash trading in the NFT market in the early stage of development. According to NFTGo.io data, there are currently 1,075 projects in the NFT market that involve fraudulent transactions, and more than 82,000 addresses are involved in fraudulent transactions. In addition, the number of transactions on the entire network has reached more than 250,000 times.
In the market, some project parties will “pull the market” by brushing to achieve marketing effects. This is essentially a behavior of deceiving users with data, but there are many reasons and methods for washing trading that cannot be generalized. For example, among the top ten projects with a high degree of fraudulent transactions, there are not only some unknown projects but also potential blue-chip projects Meebits. It shows that, at this stage, the transaction of brushing orders cannot evaluate the quality of a project, but it can still help us understand the composition of the transaction volume of a project. If the chips in the transaction volume are too “centralized”, investors should Treat this situation with caution. For example, the picture below shows the top ten projects involving fraudulent transactions. R*** and T*** ranked first and second in terms of transaction ratios.
Most brush trades are transient, high-frequency, and usually run over a long period of time. According to NFTGo.io data, the period required to complete a round of NFT transactions is 5 minutes. Over time, first, prices may fluctuate, and second, other participants may steal profits. Understanding the above purpose, we can identify the key to the transaction based on behavior – the buyer and the seller are often a single or multiple addresses under the control of the same person. Just as people discovered Melania’s washing trading, we can also capture traces of washing trading from the chain.
It is interesting that we can see that the histogram of closed-loop time does not decrease linearly, the number of closed loops over one day, three days, etc. is more than the number of time periods such as 1, 1.5, 2 hours, etc. This may be related to another pattern of money laundering transactions we found. For example, when studying cryptokitties, axie-infinityeth and other projects, a large number of closed-loop and repeated addresses (such as 0xb1690c08e213a35ed9bab7b318de14420fb57d8c have more than half a million closed-loop transactions) were found in their transaction records, showing significant money laundering transaction characteristics. However, once the closed-loop criterion is limited to the hourly level, the system fails to detect closed-loops, which is a departure from the “high frequency” characteristic of most of the trades we have previously described.
Why is a closed-loop trade within a day also a small high?
The study found that the transactions in these projects have a characteristic: usually, a certain address trades all NFTs in its own assets once a day (this number is usually large, on the order of thousands or tens of thousands), and Trading back the next day forms a closed loop. Because the base of NFTs is large enough, such seemingly “slow” transactions can often generate huge transaction volumes, which in turn creates a false picture of market prosperity.
Purpose of Wash Trading
At present, the number of NFT issuance projects and the daily transaction volume of Ethereum are among the top of all public chains. To a certain extent, this will also make it easier to hide the traces of the transaction. Washing trading on Ethereum will also consume a lot of gas fees, and the high cost of brushing orders has also reversely screened the implementers of washing trading. These people have some common characteristics, such as sufficient funds and clear strategies.
Generally speaking, there are only 0 and countless times of washing trading, and the affected projects are also relatively wide, some of which are malicious behaviors of the project party, and some are self-driven behaviors of users.
There are three main purposes for the transaction of NFTs in the market: First, for hype, the transaction volume and price of designated projects will be raised through funds to attract players to enter the game. The second is for money laundering. For example, some hackers flow “unclean money” into the NFT market and then transfer it out, thus completing the process of white laundering. The third is to obtain $LOOKS income and enhance the value of their NFTs. For example, it is more common that some people will perform multiple transaction transactions in order to obtain $LOOKS by brushing orders on LooksRare, such as two round-trip transactions each time, every day. once. Therefore, frequent transactions much higher than the floor price, abnormal transaction records under the same address of a single NFT, etc. can be used as the basis for identification.
How to Identify and Catch Wash Trading
We enumerate the on-chain behavior of the traders who are brushing orders by assuming the most basic brushing path.
First, the same user may create multiple sub-wallets for transactions between their own wallets. Before preparing for the transaction, these wallets need to have enough funds to interact with each other. Some brush traders will transfer money directly from the parent wallet, resulting in a “one-to-many” structural association, so we capture this behavior based on this behavior. Multiple sub-wallets (linked wallets) are created by the user. At present, we can carry out “traceable” transactions through the data on the chain, and trace the original mother wallet to determine the identity of the “Washer”, but once the funds enter the off-site, the chain “breaks”.
Second, wallet correlation clustering can be performed to capture mutual closed-loop transactions between wallets. If two or more wallets have been conducting uninterrupted high-frequency transactions and have not interacted with other wallets, we can also determine its suspicion. By performing limited queries on these two types of on-chain behaviors, some mechanical washing trading can be identified, so as to reversely identify sub-wallets.
Third, check the historical transaction records under a certain NFT. The same NFT has been traded many times at a price much higher than the floor price in a short period of time, and the transaction price position has not changed significantly.
These are the key actions that traders use to accomplish their goals (such as raising the floor, washing transaction volume, etc.); in addition to accomplishing their goals, they need to consider reducing intermediate losses, so we see that Looksrare has zero in some time ago. The fee attracts many projects that brush the transaction volume. Through the minimal cycle of “creating multiple wallets – transferring money to each wallet – purchasing NFT from each wallet – transaction between wallets”, the transaction of the transaction is completed, and the NFT and funds are always in the hands of the transaction.
Usually, in a wash trading, the NFT and funds are returned to the trader’s initial address, or back to the trader’s wallet through an OTC transaction. Hence the loop for “primary trader – other addresses – primary trader”. We can classify and analyze transaction flow based on address clustering and priority.
Type of wash trading activity
By using data analysis and quantitative algorithms, we can subdivide the types of NFT trading activities. For example, a closed-loop transaction between two or more parties: one transaction (Sale) is an action, two closed-loop transactions constitute a binary ring, and a closed loop composed of three transactions is called a ternary ring; multiple addresses under the control of the same party abnormal transactions, etc.
According to the above classification, we find that the number of closed-loop transactions between two-membered rings is the highest, followed by three-membered rings.
Although there is currently no clear way to avoid washing trading, a solution can be found from the above link. For example, adding KYC when the sub-wallet interacts with the trading market can identify the situation of “one person with multiple wallets”, but the trading market may lose some privacy-conscious users.
With the development of the market, there are different types of wash trading, and some projects even blatantly conduct such transactions, generating false data to disrupt the market. As a Web3 user, you should learn to discriminate and use data tools to clear the fog in the NFT market and see the facts.