Reliable Stock Prediction: Data, Models, Testing

In recent years, deep learning and large language models have entered almost every discussion on stock price prediction. Many reported results look strong on paper, but often rely on clean data, cheap trading, and generous assumptions that rarely hold in real markets. This review looks at studies from 2020–2025 through three practical lenses. First, data and task design: how prices, order books, and news are collected, filtered, labeled, and aligned with the information actually available at dec