Mapping the NFT revolution: market trends, trade networks and visual
features
- URL: http://arxiv.org/abs/2106.00647v4
- Date: Mon, 20 Sep 2021 15:45:26 GMT
- Title: Mapping the NFT revolution: market trends, trade networks and visual
features
- Authors: Matthieu Nadini, Laura Alessandretti, Flavio Di Giacinto, Mauro
Martino, Luca Maria Aiello, Andrea Baronchelli
- Abstract summary: Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items.
We analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021.
- Score: 0.25861007846258416
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Non Fungible Tokens (NFTs) are digital assets that represent objects like
art, collectible, and in-game items. They are traded online, often with
cryptocurrency, and are generally encoded within smart contracts on a
blockchain. Public attention towards NFTs has exploded in 2021, when their
market has experienced record sales, but little is known about the overall
structure and evolution of its market. Here, we analyse data concerning 6.1
million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021,
obtained primarily from Ethereum and WAX blockchains. First, we characterize
statistical properties of the market. Second, we build the network of
interactions, show that traders typically specialize on NFTs associated with
similar objects and form tight clusters with other traders that exchange the
same kind of objects. Third, we cluster objects associated to NFTs according to
their visual features and show that collections contain visually homogeneous
objects. Finally, we investigate the predictability of NFT sales using simple
machine learning algorithms and find that sale history and, secondarily, visual
features are good predictors for price. We anticipate that these findings will
stimulate further research on NFT production, adoption, and trading in
different contexts.
Related papers
- The Dark Side of NFTs: A Large-Scale Empirical Study of Wash Trading [28.20696034160891]
We analyze 8,717,031 transfer events and 3,830,141 sale events from 2,701,883 NFTs.
We identify three types of NFT wash trading and propose identification algorithms.
We also provide insights from six aspects, i.e., marketplace design, profitability, NFT project design, payment token, user behavior, and NFT ecosystem.
arXiv Detail & Related papers (2023-12-19T19:29:24Z) - What Determines the Price of NFTs? [26.368626684043992]
We analyze both on-chain and off-chain data of NFT collections trading on OpenSea to understand what influences NFT pricing.
Our results show that while text and image data of the NFTs can be used to explain price variations within collections, the extracted features do not generalize to new, unseen collections.
arXiv Detail & Related papers (2023-10-03T06:09:59Z) - Cryptocurrency Portfolio Optimization by Neural Networks [81.20955733184398]
This paper proposes an effective algorithm based on neural networks to take advantage of these investment products.
A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio.
A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy.
arXiv Detail & Related papers (2023-10-02T12:33:28Z) - Identifying key players in dark web marketplaces [58.720142291102135]
This paper aims to identify the key players in Bitcoin transaction networks linked to dark markets.
We show that a large fraction of the traded volume is concentrated in a small group of elite market participants.
Our findings suggest that understanding the behavior of key players in dark web marketplaces is critical to effectively disrupting illegal activities.
arXiv Detail & Related papers (2023-06-15T20:30:43Z) - Show me your NFT and I tell you how it will perform: Multimodal
representation learning for NFT selling price prediction [2.578242050187029]
Non-Fungible Tokens (NFTs) represent deeds of ownership, based on blockchain technologies and smart contracts, of unique crypto assets on digital art forms (e.g., artworks or collectibles)
We propose MERLIN, a novel multimodal deep learning framework designed to train Transformer-based language and visual models, along with graph neural network models, on collections of NFTs' images and texts.
A key aspect in MERLIN is its independence on financial features, as it exploits only the primary data a user interested in NFT trading would like to deal with.
arXiv Detail & Related papers (2023-02-03T11:56:38Z) - Bubble or Not: Measurements, Analyses, and Findings on the Ethereum
ERC721 and ERC1155 Non-fungible Token Ecosystem [22.010657813215413]
The market capitalization of NFT reached 21.5 billion USD in 2021, almost 200 times of all previous transactions.
The rapid decline in NFT market fever in the second quarter of 2022 casts doubts on the ostensible boom in the NFT market.
By collecting data from the whole blockchain, we construct three graphs, namely NFT create graph, NFT transfer graph, and NFT hold graph, to characterize the NFT traders.
We propose new indicators to quantify the activeness and value of NFT and propose an algorithm that combines indicators and graph analyses to find bubble NFTs.
arXiv Detail & Related papers (2023-01-05T10:17:57Z) - A Game of NFTs: Characterizing NFT Wash Trading in the Ethereum Blockchain [53.8917088220974]
The Non-Fungible Token (NFT) market experienced explosive growth in 2021, with a monthly trade volume reaching $6 billion in January 2022.
Concerns have emerged about possible wash trading, a form of market manipulation in which one party repeatedly trades an NFT to inflate its volume artificially.
We find that wash trading affects 5.66% of all NFT collections, with a total artificial volume of $3,406,110,774.
arXiv Detail & Related papers (2022-12-02T15:03:35Z) - The Art NFTs and Their Marketplaces [0.0]
Non-Fungible Tokens (NFTs) are crypto assets with a unique digital identifier for ownership, powered by blockchain technology.
This paper focuses on art NFTs that change how artists can sell their products.
It also changes how the art trade market works since NFT technology cuts out the middleman.
arXiv Detail & Related papers (2022-10-23T02:17:30Z) - Probably Something: A Multi-Layer Taxonomy of Non-Fungible Tokens [62.997667081978825]
Non-Fungible Tokens (NFTs) are hyped and increasingly marketed as essential building blocks of the Metaverse.
This paper aims to establish a fundamental and comprehensive understanding of NFTs by identifying and structuring common characteristics within a taxonomy.
arXiv Detail & Related papers (2022-08-29T18:00:30Z) - Token Spammers, Rug Pulls, and SniperBots: An Analysis of the Ecosystem of Tokens in Ethereum and in the Binance Smart Chain (BNB) [50.888293380932616]
We study the ecosystem of the tokens and liquidity pools.
We find that about 60% of tokens are active for less than one day.
We estimate that 1-day rug pulls generated $240 million in profits.
arXiv Detail & Related papers (2022-06-16T14:20:19Z) - Macroscopic properties of buyer-seller networks in online marketplaces [55.41644538483948]
We analyze two datasets containing 245M transactions that took place on online marketplaces between 2010 and 2021.
We show that transactions in online marketplaces exhibit strikingly similar patterns despite significant differences in language, lifetimes, products, regulation, and technology.
arXiv Detail & Related papers (2021-12-16T18:00:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.