Co-Attention Hybrid Model Achieves Accurate Stock Price Prediction On Eight Diverse Datasets
Predicting stock price fluctuations remains a significant challenge in financial analysis, complicated by market volatility and unpredictable patterns. Yiyang Wu, Hanyu Ma, and Muxin Ge, alongside colleagues, address this problem with SPH-Net, a new deep learning framework designed to improve the accuracy of stock price forecasting. The team, including researchers from Nanyang Technological University and the University of Nebraska-Lincoln, developed a model that uses a co-attention mechanism to analyse market data, capturing both broad trends and subtle details. Through rigorous testing on eight different stock datasets, utilising key market indicators, SPH-Net consistently outperforms existing prediction models, offering a…
