This study explores the application of advanced machine learning models for market trend forecasting, incorporating external factors such as economic indicators and sentiment analysis to enhance prediction accuracy. Comparative analysis of models including Random Forest, Support Vector Machines, Gradient Boosting, and Neural Networks revealed distinct performance differences, with Gradient Boosting achieving the highest accuracy of 92.7% and the lowest mean squared error of 0.014. External factors contributed significantly to improving model precision, as evidenced by a 7.5% increase in overall forecasting accuracy. The study emphasizes the efficacy of integrating diverse machine learning algorithms and external variables in creating robust forecasting systems. These findings highlight the potential for machine learning to revolutionize market analysis and decision-making, offering practical implications for industries seeking data-driven strategies to optimize performance in dynamic economic environments.

Machine learning, market trend forecasting, economic indicators

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