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 profitable market. However, the NFT financial 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 financial 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 classification task is performed by MERLIN models, which can also be tuned according to user preferences in the inference phase to mimic different risk-return investment profiles. Experimental evaluation on a publicly available dataset has shown that MERLIN models achieve significant performances according to several financial assessment criteria, fostering profitable investments, and also beating baseline machine-learning classifiers based on financial features.
翻译:不可调用 Tokens (NFTs) 代表了数字艺术形式(如艺术作品或收藏品)上独特的加密资产(如艺术作品或收藏品)的所有权契约。 在2021年飞跃后,NFT吸引了热门爱好者和投资者的注意力,他们希望在这个有利可图的市场投放有希望的投资。然而,迄今尚未广泛探讨NFT财务业绩预测。在这项工作中,我们根据以下假设来解决上述问题:NFT图像及其文字描述是预测NFT销售价格的基本代理物。为此,我们提议MERLIN,这是旨在培训以变换器为基础的语言和视觉模型的新的多式深层次学习框架,连同图象神经网络模型,关于NFTs图像和文本的收集。MERLIN的一个关键方面是其在金融特征上的独立性,因为它只利用了对NFT交易感兴趣的用户的基本数据,即NFT图像和文字描述是用来预测NFT销售价格升级价格销售价格的价格价格销售价格的重要代理物。我们也可以在数字周期上进行一个可变现的货币成本成本评估。