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：非同质化代币（NFT）是基于区块链技术和智能合约的数字艺术形式（如艺术品或收藏品）中独特加密资产的所有权凭证。在2021年价格飙升后备受瞩目的NFT，吸引了热衷于在这一盈利市场进行有前景投资的加密货币爱好者和投资者的关注。然而，迄今为止，NFT的财务表现预测尚未得到广泛探索。本研究基于以下假设：NFT图像及其文本描述是预测NFT售价的关键代理指标。为此，我们提出MERLIN——一种新颖的多模态深度学习框架，旨在基于NFT图像和文本集合，训练基于Transformer的语言与视觉模型，并结合图神经网络模型。MERLIN的关键特性在于其独立于财务特征，仅利用NFT交易感兴趣用户所需处理的基础数据，即NFT图像和文本描述。通过学习这些数据的密集表示，MERLIN模型执行价格类别分类任务，该任务可在推理阶段根据用户偏好进行调整，以模拟不同风险收益的投资配置。在公开数据集上的实验评估表明，MERLIN模型根据多项财务评估标准均取得了显著性能，不仅促进了盈利投资，还超越了基于财务特征的基准机器学习分类器。