Machine Learning for Predicting Stock Market Movements

Abstract

Machine learning (ML) techniques are increasingly employed in the financial sector for predicting stock market movements, given their capacity to uncover complex patterns from historical data. Unlike traditional statistical models, ML algorithms such as support vector machines (SVM), neural networks, and random forests adaptively learn from high-dimensional data to enhance forecasting accuracy. This paper explores the integration of ML models into stock market prediction frameworks and evaluates their performance against standard benchmarks. It also discusses the importance of feature engineering, data preprocessing, and model selection in financial forecasting tasks. The study concludes that ML provides a promising path toward more informed and dynamic financial decision-making.