A Study of Stock Market Prediction(SMP) Using Machine Learning Techniques

Abstract

The study discovers the application of machine learning techniques for predicting stock market trends, aiming to enhance forecasting accuracy and effectiveness. Traditional approaches like Fundamental Analysis and Technical Analysis are compared to current machine learning algorithms like Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Naïve Bayes (NB) in a comprehensive framework. The paper classifies data into market data and textual data and outlines pre-processing methods involving feature selection, order reduction, and feature representation to extract valuable insights. The challenges of data quality, model interpretability, and market volatility are discussed, emphasizing the need for more study and advancement. This paper aims to transform stock market prediction by using machine learning methods, providing investors and financial institutions with essential tools for making educated decisions in rapidly changing market conditions.