Machine Learning for Stock Market Prediction: A Review of Sentiment, Technical, Macroeconomic, and Fundamental Factors

Abstract

Predicting the stock market has become an important research topic in recent years, because accurate result can support investment decision and reduce financial risks.Traditional statistical models are hard to analyze in the nonlinear market, leading researchers to develop machine learning models. This paper reviews recent studies about stock prediction based on machine-learning methods from four perspectives: traditional machine learning models, sentiment analysis, technical indicators, macroeconomic and fundamental factors. The reviewed literature shows that machine learning methods can have better result than traditional models in handling complex financial data. Furthermore, sentiment information, technical indicators, and macroeconomic or fundamental factors can significantly improve prediction performance. However, each approach has limitations. The review finds that using multiple factors may relate to accurate predictions. Therefore, combining different factors through hybrid models is considered the most effective strategy. Finally, this review reveals a clear transition from single-factor prediction models to multi-factor prediction systems and provides a comprehensive understanding of current developments in stock prediction and highlights potential directions for further research.