Trend Prediction

Stock trend prediction aims to forecast future market movements, primarily using computational methods to analyze diverse data sources like financial statements, news sentiment, and order book data. Current research heavily employs deep learning architectures, including Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), Transformers, and Graph Neural Networks, often incorporating techniques like wavelet denoising and sentiment analysis to improve prediction accuracy. These advancements hold significant potential for improving investment strategies and portfolio management, while also contributing to a deeper understanding of complex market dynamics. However, challenges remain in ensuring model robustness and generalizability across different markets and time periods.