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www.nature.com/scientificreports/ Figure 7. Regression results. R2 of a linear regression fit to predict the primary sale price (a) and the adj secondary price sale 1 month after the primary sale (b) from different sets of features. Results are broken down by NFT categories. The abbreviation “feat(s).” stands for “feature(s)”. the predictability of future prices and the predictive power of different sets of features varies depending on the NFT category. The collectible category is the easiest to predict, with centrality and visual features yielding R2 ∈ [0.30, 0.36] and R2 ∈ [0.40, 0.50], respectively. These two families of features have the largest compound adj adj effect in the Art category; in the secondary sale price prediction, centrality features boost the predictive power of visual features by more than 50%. Regression coefficients of individual features for the task of secondary sale price prediction one month after the primary sale are presented in Table 1. Scientific Reports | Vol:.(1234567890) When predicting secondary sale prices, we consider only those NFTs that were sold in a secondary sale. These NFTs are the minority: less than 10% are sold at least once within one week after the primary sale, and only about 22% within 1 year (see “SI”). Using the same set of features that we selected for the price regression, we trained AdaBoost32, a binary classifier, to assess to what extent it is possible to predict whether an NFT will be sold after its primary sale (for more details see in “SI”). We find that this is possible to a certain extent. The prediction is most accurate when training and testing the classifier on Art NFTs only (F1 > 0.8), whereas the prediction is less reliable for the other categories (F1 ∈ [0.14, 0.33], see “SI”). The median price of the collection is among the strongest predictors, but not always the strongest. The prior probability of sale in the collection is also a strong signal, and centrality and visual features combined can sometimes outperform other feature combinations (e.g., in the Metaverse category). Last, the prediction is most accurate when trying to predict the occurrence of a secondary sale over longer periods of time (see “SI”). Conclusion The NFT market is less than four years old and has boomed in 2021. This paper presented the first overview of some key aspects of it by looking at the market history of 6.1 million NFT trades across six main NFT categories including art, games and collectibles. In brief, (1) we analyzed the main properties of the market, (2) we built and studied the traders and NFTs networks and found that most traders are specialised, (3) we showed that NFT collections tend to be visually homogeneous, and (4) we explored the predictability of NFT prices revealing that, while past history is as expected the best predictor, also NFT specific properties, such as the visual features of the associated digital object, help increase predictability. (2021) 11:20902 | https://doi.org/10.1038/s41598-021-00053-8 8PDF Image | Mapping the NFT revolution
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