Survey on Machine Learning Techniques for Stock Market Prediction: Models, Challenges, and Future Directions - Hake, Payal; Sonawane, Sheetal; Waghmare, Kalyani; Sanket, Dabade (International Journal of Computer Sciences and Engineering, 2025)
The stock market significantly impacts the global economy by shaping investment choices and contributing to financial stability. However, its dynamic, volatile, and non-linear nature makes stock price prediction a challenging yet essential task for investors, analysts, and researchers. Traditional forecasting methods, such as fundamental and technical analysis, often fail to capture complex market patterns. Recently, Machine Learning (ML) techniques have demonstrated effectiveness in forecasting stock prices by analyzing historical data and identifying intricate trends. This survey provides a comprehensive review of various ML models, including Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and hybrid approaches, highlighting their effectiveness in stock market forecasting. Additionally, it explores the role of sentiment analysis in price prediction, as financial news, social media, and investor sentiment significantly influence market movements. The paper discusses key challenges, optimization strategies, and recent advancements in combining ML with sentiment analysis for enhanced predictive accuracy. By analyzing existing literature and identifying research gaps, this survey offers valuable insights into stock market prediction using machine learning.