A Review of Machine Learning Applications in Market Trend Forecasting

Аннотация

This article examines the role of machine OPEN ACCESS learning (ML) techniques in market trend forecasting, SUBMITTED 30 July 2025 ACCEPTED 06 August 2025 with a focus on their advantages over traditional PUBLISHED 21 August 2025 approaches. Key algorithms are reviewed, including VOLUME Vol.07 Issue 08 2025 regression models, neural networks, gradient boosting, and hybrid architectures, along with essential data CITATION preprocessing steps such as cleaning, synthetic feature COPYRIGHT generation, and feature importance evaluation. Using © 2025 Original content from this work may be used under the terms case studies from leading financial institutions (e.g., of the creative commons attributes 4.0 License. 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. Keywords: machine learning, trading, artificial intelligence, market forecasting, finance, data analytics, economics, risk management, statistics.