Disentangled Dual-Granularity Learning for Market-Adaptive Stock Return Forecasting under Non-Stationary Environments
Deep learning has emerged as a prominent paradigm in stock return forecasting, demonstrating remarkable capabilities in extracting non-linear patterns from historical stock data. However, existing approaches often process stock features as a monolithic input with fixed temporal receptive fields. This structural inflexi...