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Home NFT

Ethereum: Nearly one in two NFT transactions would be fake in October 2022!

by Ashutosh Thakur
November 24, 2022
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Ethereum: Nearly one in two NFT transactions would be fake in October 2022!
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The NFT market didn’t really need that. Already heavily hit by the bear market, non-fungible tokens are experiencing a very sharp drop in activity. And as if that were not enough, it now appears that half of the transaction volume on the Ethereum blockchain is actually the result of fake transactions. This is in any case the conclusions of Footprint Analytics.

$389 million in fictitious transactions in October?

On the Ethereum blockchain, the month of October was the scene of 3 million transactions NFT for a total amount of 850 million dollars. Figures down for the sixth consecutive month. As shown in the table below, accessible from the platform Footprint Analytics.

Ethereum: Nearly one in two NFT transactions would be fake in October 2022!

If these sharply declining figures are worrying, some have looked into a well-known phenomenon that inflates the volume of sales: the purchase resale by one and the same person. A phenomenon also known as wash trading and which aims to artificially inflate the prices of NFT collections.

According to Anndy Lian’s analysis, of the 850 million dollars in transactions, 389 million would be fictitious transactions. That is nearly 50% of the total volume. The phenomenon has never seemed to be so present. NFT market followers will note that between September and October, the number of unique buyers and sellers increased while the number of transactions declined.

Wash trading: a scourge for the market!

If the primary objective of wash trading is to artificially overvalue the price of an asset or collection of NFTs. But this is not the only parameter that this type of activity truncates. Indeed, as wash trading increases the volume of transactions, it gives the impression of greater liquidity than it really is.

The main difficulty is that it is impossible to prevent wash trading.

To detect wash trading, it is important to carefully monitor the NFT market. But several elements can put the chip in the ear like:

  • NFT transactions at overvalued prices compared to the market price
  • Transactions placed with 0% origination fees
  • Collections whose sales volume does not correspond with the average volume observed daily
  • The close purchase and resale of an NFT by the same wallet

Anyway, the Footprint Analytics platform allows you to filter the sales volume on a platform or on a collection by removing wash trading. This will allow you to have a vision more in line with the reality of the NFT market.

Which platforms and collections are most affected?

By applying the filters which made it possible to arrive at this figure of 46% of false transactions, we also realize that nearly 80% of transactions on X2Y2 could be fictitious. An impacting figure when you know that the platform is one of the three most important in the NFT segment. If the figures are even higher on X2Y2, it is because the platform’s business model allows you to benefit from rewards on transactions. And these are allocated according to volumes. This element inevitably encourages an increase in wash trading.

Among other major players, the scourge is even greater. This methodology then makes it possible to estimate that wash trading represents 94% of transactions on the Looksrare platform. At the house of Openseathe problem is much less present since it would barely represent 5% of trading volume.

If the platforms are not equal in the face of the phenomenon, what about the collections? The methodology used also makes it possible to estimate the proportion of wash trading for the sale of each NFT collection. And some numbers are chilling. Thus, it is estimated that more than 99.5% of trades on the Dreadfulz collection correspond to wash trading. But the most popular NFT collections like Bored Ape Yacht Club (BAYC) or CryptoPunks are very little affected by this phenomenon. Perhaps because they are already sufficiently recognized.

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