Market Phases and Price Discovery in NFTs: A Deep Learning Approach to Digital Asset Valuation

This study introduces the Channel-wise Attention with Relative Distance (CARD) model for NFT market prediction, addressing the unique challenges of NFT valuation through a novel deep learning architecture. Analyzing 26,287 h of transaction data across major marketplaces, the model demonstrates superior predictive accuracy compared to conventional approaches, achieving a 33.5% reduction in Mean Absolute Error versus LSTM models, a 29.7% improvement over Transformer architectures, and a 30.1% enhancement compared to LightGBM implementations. For long-term forecasting (720-h horizon), CARD maintains a 35.5% performance advantage over the next best model. Through SHAP-based regime analysis, we identify distinct feature importance patterns across market phases, revealing how liquidity metrics, top trader activity, and royalty dynamics drive valuations in bear, bull, and neutral markets respectively. The findings provide actionable insights for investors while advancing our theoretical understanding of NFT market microstructure and price discovery mechanisms.