From the Artist’s Contract to the Blockchain Ledger: New Forms of Artists’ Funding Using NFTs, Fractional Equity, and Resale Royalties

Haaften-Schick, Lauren van; Whitaker, Amy

doi:10.2139/ssrn.3842210

Cited by 17 publications

(15 citation statements)

References 55 publications

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“…NFT markets research studies: As mentioned in the previous section NFT markets are new, but have seen exponential growth. The research on these markets has not followed suit yet and has mainly focused on the underlying blockchain protocols and standards, copyright regulations and their impact on the world of art [8][9][10][11][12] . Empirical analysis of the properties and structure of NFT markets is still limited and has mainly focused on specific platforms and the relationship between pricing, scarcity and cryptocurrencies [13][14][15] .…”

Section: Background and Related Workmentioning

confidence: 99%

Spotting Anomalous Trades in NFT Markets: The Case of NBA Topshot

Pelechrinis¹,

Liu²,

Krishnamurthy³

et al. 2022

Preprint

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Non-Fungible Token (NFT) markets are one of the fastest growing digital markets today, with the sales during the third quarter of 2021 exceeding $10 billions! Nevertheless, these emerging markets -similar to traditional emerging marketplaces -can be seen as a great opportunity for illegal activities (e.g., money laundering, sale of illegal goods etc.). In this study we focus on a specific marketplace, namely NBA TopShot, that facilitates the purchase and (peer-to-peer) trading of sports collectibles. Our objective is to build a framework that is able to label peer-to-peer transactions on the platform as anomalous or not. To achieve our objective we begin by building a model for the profit to be made by selling a specific collectible on the platform. We then use RFCDE -a random forest model for the conditional density of the dependent variable -to model the errors from the profit models. This step allows us to estimate the probability of a transaction being anomalous. We finally label as anomalous any transaction whose aforementioned probability is less than 1%. Given the absence of ground truth for evaluating the model in terms of its classification of transactions, we analyze the trade networks formed from these anomalous transactions and compare it with the full trade network of the platform. Our results indicate that these two networks are statistically different when it comes to network metrics such as, edge density, closure, node centrality and node degree distribution. This network analysis provides additional evidence that these transactions do not follow the same patterns that the rest of the trades on the platform follow. However, we would like to emphasize here that this does not mean that these transactions are also illegal. These transactions will need to be further audited from the appropriate entities to verify whether or not they are illicit.

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Section: Background and Related Workmentioning

confidence: 99%

Spotting Anomalous Trades in NFT Markets: The Case of NBA Topshot

Pelechrinis¹,

Liu²,

Krishnamurthy³

et al. 2022

Preprint

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“…The highly volatile NFT prices and sudden popularity led many people to create NFTs and in turn, promote and sell them for a profit. The most significant impact of NFTs has been on how they transformed the art world [8,15]. NFTs have allowed artists to sell their art outside the gate-keeping systems and taste-hierarchies [8].…”

Section: Introductionmentioning

confidence: 99%

“…The most significant impact of NFTs has been on how they transformed the art world [8,15]. NFTs have allowed artists to sell their art outside the gate-keeping systems and taste-hierarchies [8].…”

Section: Introductionmentioning

confidence: 99%

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TweetBoost: Influence of Social Media on NFT Valuation

Kapoor¹,

Guhathakurta²,

Mathur³

et al. 2022

Preprint

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NFT or Non-Fungible Token is a token that certifies a digital asset to be unique. A wide range of assets including, digital art, music, tweets, memes, are being sold as NFTs. NFT-related content has been widely shared on social media sites such as Twitter. We aim to understand the dominant factors that influence NFT asset valuation. Towards this objective, we create a first-of-its-kind dataset linking Twitter and OpenSea (the largest NFT marketplace) to capture social media profiles and linked NFT assets. Our dataset contains 245,159 tweets posted by 17,155 unique users, directly linking 62,997 NFT assets on OpenSea worth 19 Million USD. We have made the dataset public. 3 We analyze the growth of NFTs, characterize the Twitter users promoting NFT assets, and gauge the impact of Twitter features on the virality of an NFT. Further, we investigate the effectiveness of different social media and NFT platform features by experimenting with multiple machine learning and deep learning models to predict an asset's value. Our results show that social media features improve the accuracy by 6% over baseline models that use only NFT platform features. Among social media features, count of user membership lists, number of likes and retweets are important features.* * denote equal contribution 3

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“…[13]. An overview of the implications that NFTs have on art is done in references [14,15], where the blockchain-based NFTs are presented as an evolution of the 1917 Artist's contract, introduced in 1971 to share secondary sale royalties with the artist. Other studies have focused on a limited number of similar NFTs, such as CryptoKitties [16,17], or NFTs offered on Decentraland [18], SuperRare [19,20], or a combination of Decentraland, Cryptopunks, and Axie [21].…”

Section: Introductionmentioning

confidence: 99%

Mapping the NFT revolution: market trends, trade networks and visual features

Nadini,

Alessandretti,

Di Giacinto

et al. 2021

Preprint

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Non Fungible Tokens (NFTs) are digital assets that represent objects like art, videos, in-game items and music. They are traded online, often with cryptocurrency, and they are generally encoded as smart contracts on a blockchain. Media and public attention towards NFTs has exploded in 2021, when the NFT art market has experienced record sales while celebrated new star artists. However, little is known about the overall structure and evolution of the NFT market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs generating a total trading volume of 935 millions US dollars. Our data are obtained primarily from the Ethereum and WAX blockchains and cover the period between June 23, 2017 and April 27, 2021. First, we characterize the statistical properties of the market. Second, we build the network of interactions and show that traders have bursts of activity followed by inactive periods, and typically specialize on NFTs associated to similar objects. Third, we cluster objects associated to NFTs according to their visual features and show that NFTs within the same category tend to be visually homogeneous. Finally, we investigate the predictability of NFT sales. We use simple machine learning algorithms and find that prices can be best predicted by the sale history of the NFT collection, but also by some features describing the properties of the associated object (e.g., visual features of digital images). We anticipate that our analysis will be of interest to both researchers and practitioners and will spark further research on the NFT production, adoption and trading in different contexts.

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From the Artist’s Contract to the Blockchain Ledger: New Forms of Artists’ Funding Using NFTs, Fractional Equity, and Resale Royalties

Cited by 17 publications

References 55 publications

Spotting Anomalous Trades in NFT Markets: The Case of NBA Topshot

Spotting Anomalous Trades in NFT Markets: The Case of NBA Topshot

TweetBoost: Influence of Social Media on NFT Valuation

Mapping the NFT revolution: market trends, trade networks and visual features

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