Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction

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). In the spotlight after skyrocketing in 2021, NFTs have attracted the attention of crypto enthusiasts and investors intent on placing promising investments in this protable market. However, the NFT nancial performance prediction has not been widely explored to date. In this work, we address the above problem based on the hypothesis that NFT images and their textual descriptions are essential proxies to predict the NFT selling prices. To this purpose, 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 nancial features, as it exploits only the primary data a user interested in NFT trading would like to deal with, i.e., NFT images and textual descriptions. By learning dense representations of such data, a price-category classication task is performed by MERLIN models, which can also be tuned according to user preferences in the inference phase to mimic dierent risk-return investment proles. Experimental evaluation on a publicly available dataset has shown that MERLIN models achieve signicant performances according to several nancial assessment criteria, fostering protable investments, and also beating baseline machine-learning classiers based on nancial features. CCS CONCEPTS • Computing methodologies ! Machine learning; Natural language processing; Computer vision.