Exploring Machine Learning Methods for Stock Market Prediction: A Review

Abstract

Stock market prediction has long been a challenging and highly dynamic field due to the inherent volatility and complexity of financial markets. Traditional methods, such as auto regressive models and moving averages, have limitations in capturing the non-linear, everchanging behavior of stock prices. Machine learning (ML) has emerged as a powerful tool, offering the ability to analyze large volumes of data and uncover hidden patterns. This paper presents a comprehensive review of various machine learning techniques, including Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and hybrid models, focusing on their application in stock market prediction. We examine the strengths, weaknesses, and challenges of these techniques and suggest directions for future research. The paper concludes by discussing potential avenues for improving predictive accuracy, particularly through the integration of advanced deep learning models, real-time data processing, and sentiment analysis.