Stock Trend

Predicting stock market trends aims to forecast future price movements, informing investment decisions and potentially improving portfolio management. Current research heavily utilizes machine learning, employing diverse architectures like Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), Gradient Boosting Machines (GBMs), and more recently, quantum-enhanced LSTMs. These models are applied to various data sources, including fundamental financial data, social media sentiment (e.g., from Twitter), and news articles, often incorporating natural language processing techniques. Improved accuracy in trend prediction holds significant implications for both academic understanding of market dynamics and practical applications in finance.