Since its launch, Blur has taken the NFT world by storm with its trader-friendly interface, 0% transaction fees, rock-bottom royalty fees, and $BLUR token airdrop. This has helped it eclipse Opensea as the top marketplace in the Ethereum ecosystem, accounting for 70% of the overall market share and even up to 90% for some of the most highly traded collections.

Along with its meteoric rise, questions have been swirling around on the effects of Blur on the entire ecosystem. In this article, we take a deep dive into whale trading and token farming activities and characterize their effect on the NFT ecosystem.

Contrary to the article written by dune wizard and crypto data expert Hildobby, analyzing wash trading behaviour in Blur, we find that there exists a significant amount of wash trading activities in Blur. With the emergence of large Whales wash-trading NFTs to farm $BLUR tokens, floor prices of popular NFT collections are highly at risk of being manipulated by Whale bidding and listing behaviors. Newcomers and collectors should be cautious when trading in markets dominated by cliques of connected whales.

Wash trading is a type of market manipulation in which an individual or group of individuals artificially inflate trading volumes by buying and selling assets to themselves, giving the appearance of increased demand and activity. This can attract other buyers, leading to even higher prices, and can also create the illusion of a healthy market.

In Hildobby’s article on the 10th of February, he mentioned that 11% of the traded volume was flagged as wash trading. Since then, Blur launched season 2 of its airdrop where all bidding and listing points are double until the 1st of May. This led to an influx in trading activities and seems to have explained the increase in wash trading volume to 19.5% by the 28th of March.

This volume is still relatively understated. Hildobby’s approach of filtering for wash trades strongly assumes that all NFTs are non-fungible and back-and-forth trades are flagged as wash trades only if there is a matching trade with seller and buyer inverted for the exact same NFT.

However, this assumption does not hold for 2 reasons:

1. A lot of new collections are minting multiple copies of NFTs with similar attributes e.g. Gitcoin Presents, Owls, or Checks. While there might be some differences in trait rarity, they do not really command a premium on the market.

2. As revealed by the current trading activities ongoing at Blur, given an external incentive to flip, rather than to collect NFTs, such tokens become commoditized and have fairly few distinguishing characteristics. In the eyes of a whale trader churning through NFT trades to make market volume and top the charts of the Blur leaderboard, rarity be damned and everything (around the floor price) looks the same in the blur blender.

All this leaderboard talk got us thinking. What if we revised the wash trading methodology to consider more complex cyclical patterns and relax the assumption of “non-fungibility” - all NFTs within a collection can be treated as the same?

Any set of cyclic transactions (length lesser than 10) within a collection, completed within a week is deemed as wash traded. A simple example would be A→ B→ A. Although this can be by coincidence, it is very improbable, this results in no change of ownership of the NFT while recording trading volume, which is inorganic.

This method also helps to capture more complex patterns such as trading cliques where NFTs can be traded across multiple related wallets. This is particularly relevant in the case of Blur as farmers trade/bid floor NFTs back and forth across multiple sets of addresses, to accumulate points quickly for the airdrop.

Based on the revised methodology, we find that 38% of the volume on Blur has been wash traded.

While our methodology might overstate certain genuine cases of cyclical transaction activities, it still shows that the percentage of wash trading in Opensea is low and mostly aligns with other methods before the emergence of Blur, suggesting that it is indeed the incentive structure of Blur that is resulting in flipping of NFTs across collections, contributing to the high wash-traded volume.

The extent to which wash trading or $BLUR token farming affects collections vary relatively significantly. Since reward points are given based on bids of top collections closer to the floor, market makers are incentivized to trade on more liquid collections. This usually means top blue-chip collections like the Bored Apes, Azuki, Doodles, CloneX, and Moonbirds to the hottest new mints like Owls, Checks, or Nakamigos. There’s also less incentive to trade on contracts that enforce the royalty fees like the Memeland collections.

Here’s us aggregating wash trading statistics across a broad range of collections:

Consistent with our expectations, a significant volume of wash trades is happening within blue-chip collections and emerging collections, while a much lower volume is happening across less active collections or those that enforced royalty.

On a high-level analysis, we do not find a direct relationship between wash trading activity and price changes. Interestingly, highly traded blue chips like Azukis or MAYC did not result in a significant decline in price, whilst in the case of Doodles or Moonbirds, it seems to act as a catalyst to news that was perceived to be unfavorable by their holder base, exacerbating the decline in prices.

The high percentage of wash trades within specific collections raises suspicion of market manipulation and collusion amongst whales. We examine this in the section below.

To efficiently farm points on blur, it helps to have a partner. In the ideal case, both parties would take opposite sides of the order book. With a significant portion of assets under control, the trading clique could essentially dictate the market price and activity of a given collection. In fact, certain collections have been blacklisted by blur, and the points given to those collections were revoked as they were deemed to be specifically created for blur farming.

Let’s examine the addresses responsible for the majority of the wash trading behaviour:

While there are other cliques engaging in wash trading behavior, 1 address stands out among the rest, 0x020ca66c30bec2c4fe3861a94e4db4a498a35872 (MACHIBIGBROTHER.ETH), an NFT Whale with a dubious background as documented in Zachxbt’s investigative piece on Machi’s numerous pump and dump projects.

The other top 9 addresses have all interacted directly with MACHI on at least one occasion detected to be wash trading, suggesting that there’s likely to be collusion among a group of addresses.

In total, we detected close to 900 addresses (out of 5k detected wash trader addresses) to have wash traded with machi on at least 1 occasion.

85% of wash trading activity on blur is found to be connected to MACHI either in a cyclic transactional pattern or the addresses has been in a cyclic pattern with MACHI on 1 separate occasion before. In terms of all volume on blur, 59% originates from MACHI and related connections.

We examine the transaction patterns of trading wallets across various large collections on a given day to illustrate the connections between the flagged wallets.

For a highly wash-traded collection like MAYC, there is a tight clique of wallets with numerous trades within the clique. Explore the relationship in more detail at the following link:

https://www.motif.gl/explorer/5504ffa3-92a7-4487-8fad-bc0974f0bfa9/editor

In a more normally traded collection, the network is more diffuse and less concentrated:

https://www.motif.gl/explorer/c7820d34-4a3b-4ee8-ac78-3437f99fd27b/editor

Examining the co-occurrence network across blue chip collections (MAYC, Azuki, Moonbirds, Doodles, Renga, Pudgy Penguin, Clone X) from Feb 22nd to present and filtering out edges with less than 5 counts of interactions, we get the following network:

Https://www.motif.gl/explorer/fe97b021-8d9c-479f-a9c7-281272e635c6/editor

Green nodes are flagged wash trading nodes not part of machi’s clique, red nodes are flagged nodes that have interacted with machi, and blue are normal users

The top influential nodes by degree are :

• [Machi] 0x020ca66c30bec2c4fe3861a94e4db4a498a35872

• [Ghost] 0xa7078ddf23d29e9d6e54e34909cd2ac1b33a67c5

• [Most interactions on Clone X & Moonbirds, not much interaction with machi - seems to have emptied out] 0x9c81522976f5eebba38c60d36c63facb5e9e9f23

• [Within Machi’s Close Circle] 0x97c7d94d01bcbc41b80ef7cc8c5bebc3d11c6a20

Being responsible for a large part of market-making behaviour, large Whale cliques dominate trading activity and essentially set market prices. This is not specific to Machi and there might be other cliques involved in the trading of specific collections for example, [0x9c81522976f5eebba38c60d36c63facb5e9e9f23] & [0x9082d2785155D64c9E87812B92958ACff4fDfBa9] for CloneX and Moonbirds.

Gitcoin presents a collection launched to celebrate and commemorate quadratic funding - a mathematical foundation for directing resources towards projects in a way that benefits the greatest number of people, caught the attention of Whales, and became a wash trading collection of choice.

With the attention of whale traders, it soon became monopolized. At one point in time, Machi held about 3743 Gitcoins, or more than 40% of the entire collection. He subsequently dumped it on the open market for a significant loss.

Here’s a table summarizing the possible influence Machi has had on specific NFT collections during the past month:

Similar trading patterns are likely to occur for other newly hyped-up collections and collectors should proceed extremely cautiously when trading on them.

To further assist genuine collectors in the space, we created a blur leaderboard tracker, that summarizes the positions, activity, and holdings of the top-ranking wallets.

The Team behind the Article:

Timothy Lin, CTO @ Néah

Swan Lin, Data Scientist @ Néah

Aaron Isaac, CEO @ Néah