A Survey on Stock-Market Prediction using Machine Learning

Abstract

The stock market is a complex and dynamic system that plays a crucial role in the global economy. Over the years, researchers have used a variety of methods to study and understand the stock market, including statistical analysis, econometric modeling, and machine learning. This survey paper provides an overview of the current state of research on the stock market, with a focus on the use of machine learning techniques. The paper begins by discussing the various types of data that are commonly used in stock market research, including financial statements, news articles, and social media posts. It then reviews the different machine learning approaches that have been applied to stock market prediction, including supervised learning, unsupervised learning, and reinforcement learning. Overall, the survey aims to provide a comprehensive and up-to-date overview of the field of stock market prediction using machine learning.