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Existing works on valuing digital assets on the Internet typically focus on asingle asset class. To promote the development of automated valuationtechniques, preferably those that are generally applicable to multiple assetclasses, we construct DASH, the first Digital Asset Sales History dataset thatfeatures multiple digital asset classes spanning from classical toblockchain-based ones. Consisting of 280K transactions of domain names(DASH_DN), email addresses (DASH_EA), and non-fungible token (NFT)-basedidentifiers (DASH_NFT), such as Ethereum Name Service names, DASH advances thefield in several aspects: the subsets DASH_DN, DASH_EA, and DASH_NFT are thelargest freely accessible domain name transaction dataset, the only publiclyavailable email address transaction dataset, and the first NFT transactiondataset that focuses on identifiers, respectively.  We build strong conventional feature-based models as the baselines for DASH.We next explore deep learning models based on fine-tuning pre-trained languagemodels, which have not yet been explored for digital asset valuation in theprevious literature. We find that the vanilla fine-tuned model already performsreasonably well, outperforming all but the best-performing baselines. Wefurther propose improvements to make the model more aware of the timesensitivity of transactions and the popularity of assets. Experimental resultsshow that our improved model consistently outperforms all the other modelsacross all asset classes on DASH.

Digital Asset Valuation: A Study on Domain Names, Email Addresses, and NFTs