NFT Wash Trading

Quantifying suspicious behaviour in NFT markets

Victor von Wachter

1

, Johannes Rude Jensen

1,2

, Ferdinand Regner

3

, and Omri

Ross

University of Copenhagen, Denmark

ity of on-chain volumes, given the absence of identity requirements and

the ease with which agents can control multiple addresses. We exam-

ine potentially illicit trading patterns in the NFT markets from January

2018 to mid-November 2021, gathering data from the 52 largest collec-

tions by volume. Our findings indicate that within our sample 3.93% of

addresses, processing a total of 2.04% of sale transactions, trigger suspi-

cions of market abuse. Flagged transactions contaminate nearly all col-

lections and may have inflated the authentic trading volumes by as much

as $149,5m for the period. Most flagged transaction patterns alternate

between a few addresses, indicating a predisposition for manual trading.

Keywords: DeFi · NFT · Blockchain · Wash trading · Graph analysis

1 Introduction

A non-fungible token (NFT) is a unique digital representation of a digital or

physical asset. While the NFT standard is used widely to designate ownership

of artefacts such as domain name registrations or concentrated liquidity positions

in constant function market makers (CFMM) [1], the arguably most recognized

use of the NFT standard is within the representation and trade of digital art

and collectibles. Here, the NFT is typically used to represent the ownership of

a digital image externally stored, either on a server or, more commonly, on cen-

sorship resistant distributed file systems such as the Interplanetary File System.

to the emergence of NFT markets, facilitating primary and secondary trading,

themselves implemented as smart contracts, enable users to sell and purchase

NFTs in two ways: as a fixed-price sale or auction, in which competing bids are

locked by the smart contract together with the NFT until a winner is found and

the auction is cleared. With the admission of NFTs into popular culture, trade

year, a surge of over 29,060%

.

Users typically connect to the permissionless markets through public-key

cryptography capable of generating an arbitrary number of addresses [3]. As

addresses a single agent can control, we conjecture that adversarial agents likely

employ a mixture of manual trading and bots to trade NFTs between clusters

of addresses in their control. This behaviour serves the strategic purpose of

artificially inflating the trade volume of a given NFT, creating an impression

of desirability to uninformed traders [4]. The uninformed traders, looking for a

great opportunity to buy a ‘hot’ NFT will interpret the transaction volume as

an authentic expression of interest from other collectors and immediately place

a bid or purchase the NFT at an artificially inflated price.

Furthermore, novel markets tend to be driven by a volatile search for suit-

able pricing models [5]. This is undoubtedly the case for the blossoming crypto

markets, remains unclear. To fill the gap, in this paper we study activities be-

tween addresses participating in the NFT markets on Ethereum. We pursue the

research question: ‘To what extent does wash trading occur in smart contract-

based NFT markets on Ethereum, and to which extent does this practice dis-

tort prices?’. Conceptualizing trading patterns as a graph and proposing two

detection algorithms, we identify 2.04% as the lower bound of suspicious sale

transactions that closely follow the general definition of wash trading.

4

2 Literature Review

Wash trading is a well-known phenomenon in traditional financial markets and

refers to the activity of repeatedly trading assets for the purpose of feeding mis-

graphs on order book data [12]. The literature on the identification of wash trad-

ing patterns in the cryptocurrency markets primarily emphasizes CLOB models

on decentralized exchanges [9] and centralized exchanges, where wash trading

practices have been shown to be especially prevalent[6, 7, 8]. Perhaps because

the introduction of CFMMs has nearly eliminated the efficacy of wash trading

in smart contract-based markets for fungible assets, research on NFTs tends to

emphasize either market dynamics and pricing [13, 14] or the technical design

considerations [15, 16]. Thus far, little academic research has examined market

abuse in smart contract-based NFT markets [17].

3 Methodology

tography [18]. Any user on the network can generate a public/private key pair,

which can subsequently be used to generate an arbitrary number of addresses.

This design presents a fascinating paradox from the perspective of identifying

market abuse: Pseudonymous identities are essential in protecting the privacy of

benevolent users but, at the same time, they allow adversarial agents to hide in

plain sight. Yet, due to the strict ordering of transactions and unique properties

of NFT markets, blockchain transaction data presents a powerful and unique

opportunity for pattern detection utilizing graph-based algorithms [9, 19, 20]

and address clustering [21, 22].

52 leading ERC721 NFT collections on the Ethereum blockchain by trading

volume, covering a period between the 1st of January 2018 until 21st November

total trading volume (49.5%)

on all NFT markets since the first block of the

indicating that a change of ownership has been recorded on the blockchain,

and ‘transfer’ events indicating that the NFT has been transferred from one

address to another. The dataset was subsequently enriched with (I) historic USD

prices for settlements in crypto and stablecoins via the Coingecko API and (II)

blockchain-specific data via the Etherscan services. (III) To maintain an accurate

overview of the NFT markets in the dataset, we collected the deployment date of

the four largest NFT on-chain markets manually (Foundation, OpenSea, Rarible,

Superrare) and matched the deployment dates with the event emissions.

It should be noted that the dataset collected for this analysis pertains only

to operations conducted within the Ethereum Virtual Machine (EVM), meaning

NFT as a directed multigraph G

and E is the set of transactions between addresses. The direction of the edges

is given by the transaction flow from sender to receiver, identified by the trans-

action hash. The weight of the edges represents the USD price at the time of

the transaction. This denotation is amenable to the identification of clusters in

which a sequence of transactions leads to no apparent position change for any of

the addresses involved. Topologically, these patterns form closed cycles. Utilizing

Deep-First-Search-Algorithm [23] we identify closed cycles within the data set.

We adjust and iterate the algorithm, using the temporal distance between trans-

6

While we identify suspicious activities in all NFT collections (Table 2, Ap-

ranges from 0.19% to 60.93%, indicating that adversarial agents tend to target

specific collections for illicit practices. In general, we observe a predisposition

for simple trading patterns. 60.6% of the identified clusters are simple variations

with two transactions (equivalent to examples 2-4 in Figure 1). Complex varia-

tions of three (8.7%) or more than three transactions are less common (30.7%).

However, we find no signs of self-directed trades. Cyclical patterns are conducted

at relatively rapid intervals. Figure 3 illustrates the elapsed time from the first to

7

this is also matched by a high amount of organic traffic. Increasing the price

illicit trading [4]. Analysing if the average price is inflated through wash trading

practices, we find that the subsequent sale after a detected wash trade has, on

average, an increased price of 30.53%. However, a regression on panel data to

measure the impact on the price, led to insignificant results for a majority of

collections. Looking into external factors, we found that age has a strong positive

relationship with the price. While we expected an inverse relationship of the gas

price, which impacts the costs per transaction, with the NFT price, we found the

paper increase over time, smart contract-based NFT platforms may consider

the implementation of obligatory or voluntary identification initiatives. Alterna-

be implemented. In our sample self-directed trades have been non-existent, with

indicates successful countmeasures at smart contract or frontend level. Never-

theless, any such attempt at introducing restrictions or limitations may stifle

organic market activity and will inevitably create a cat and mouse game, as de-

velopers and wash traders race to identify and create increasingly sophisticated

patterns.

increased data ubiquity. Decentralized NFT markets are transparent, however

NFT data is very diverse and difficult to retrieve. Fees potentially play a big role

in preventing wash trading, as long as the rewards or incentives are less than the

cost-of-attack. Fees on NFT markets are substantial. Fraudulent agents are less

likely to perform a wash trade if they are losing several percentages with every

transaction. Admittedly, this does neither stop marketplaces itself to perform

wash trades nor prevents private offset agreements between the trader and a

marketplace.

we argue that wash trading may be less common than what industry observers

indicate that (I) adversarial agents exhibit a clear preference towards fast and

simple cyclical patterns, (II) the level of suspicious activity varies significantly

across NFT collections, (III) illicit activity could still be done in a manual fash-

ion, and (IV) the activities do not necessarily produce the intended price impact,

as other exogenous factors such as age and sentiment are more relevant to price

discovery.

As a theoretical contribution, we add descriptive knowledge to an emerging

field of research where scientific studies are scarce. We contribute to the grow-

ing literature on the identification of illicit market behavior in centralized and

decentralized crypto markets, by conducting the first in-depth examination of

NFT wash trading on the Ethereum blockchain. We contribute empirical statis-

tics of fraudulent behaviour and a set of suspicious transaction graphs to foster

the understanding of wash trading in increasingly financialized NFT markets.

The valuable insights we generate for practitioners are twofold: First, we provide

valuable insights to prevent collectors from buying NFTs that are potentially in-

the standards for the wider NFT ecosystem. Further research opportunities are

manifold and include studying NFT markets on other blockchains as well as