A Review of Machine Learning Applications in Market Trend Forecasting

This article examines the role of machine learning (ML) techniques in market trend forecasting, with a focus on their advantages over traditional approaches. Key algorithms are reviewed, including regression models, neural networks, gradient boosting, and hybrid architectures, along with essential data preprocessing steps such as cleaning, synthetic feature generation, and feature importance evaluation. Using case studies from leading financial institutions (e.g., Renaissance Technologies, JPMorgan Chase), the paper highlights how ML enhances forecast accuracy, optimizes risk management, and accelerates decision-making processes. Several challenges are identified, including dependence on data quality, the risk of overfitting, high computational costs, and the interpretability of complex models. The paper also outlines promising directions for development, such as the integration of transfer learning methods, generative adversarial networks (GANs), and the adaptation of algorithms to non-stationary financial data. The findings emphasize the transformative potential of ML in the context of increasing financial market volatility. This article will be particularly valuable for professionals in finance, especially those engaged in trading and stock market operations, offering practical guidance on selecting optimal ML methods for financial applications. Theoretical insights provided may also serve as a basis for further academic and applied research in artificial intelligence.