Deep context-attentive transformer transfer learning for financial forecasting.

ABSTRACT: This study presents 2CAT (CNN-Correlation-based Attention Transformer), a deep learning model for financial time-series forecasting. The model integrates signal decomposition, convolutional layers, and correlation-based attention mechanisms to capture temporal patterns. A transfer learning framework is incorporated to enhance generalization across markets through pretraining, encoder freezing, and fine-tuning. Evaluation on six stock indices-Dow Jones Industrial Average (DJIA), Nikkei 225 (N225), Hang Seng Index (HSI), Shanghai Stock Exchange (SSE), Bombay Stock Exchange (BSE), and the Stock Exchange of Thailand (SET)-demonstrates strong predictive accuracy. On DJIA, 2CAT records an MSE of 0.0655, MAE of 0.2023, and R² of 0.9169, outperforming Deep-Transformer, which yields an M

SUBMITTER: Feng L 

PROVIDER: S-EPMC12453852 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature