More than price prediction: a multimodal end-to-end interpretable deep learning (MEID) framework for NFT investment

Abstract Nonfungible tokens (NFTs) have become highly sought-after assets in recent years, exhibiting potential for profitability and hedging. The large and lucrative NFT market has attracted both practitioners and researchers to develop NFT price-prediction models. However, the extant models have some weaknesses in terms of model comprehensiveness and operational convenience. To address these research gaps, we propose a multimodal end-to-end interpretable deep learning (MEID) framework for NFT investment. Our model integrates visual features, textual descriptions, transaction indicators, and historical price time series by leveraging the advantages of convolutional neural networks (CNNs), adopts integrated gradient (IG) to improve interpretability, and designs a built-in financial evaluation mechanism to generate not only the predicted price category but also the recommended purchase level. The experimental results demonstrate that the proposed MEID framework has excellent properties in terms of the evaluation metrics. The proposed MEID framework could help investors identify market opportunities and help NFT transaction platforms design smart investment tools and improve transaction volume.