Paper Contents
Abstract
With the increasing use of machine learning techniques for stock price detection has gained significant attention in recent years. This survey aims to provide an overview of the various methodologies and approaches employed in this domain. The study analyzes the application of machine learning algorithms for stock price prediction, focusing on their performance, accuracy, and effectiveness. Different types of features used in modeling, such as technical indicators, sentiment analysis, and financial news, are also explored. Furthermore, this survey presents an evaluation of the challenges and limitations encountered in stock price detection using machine learning, including data availability, model over fitting, and market volatility. By examining the existing literature and research advancements, this survey contributes to a comprehensive understanding of the current state of stock price detection using machine learning and identifies potential areas for future research and improvement.
Copyright
Copyright © 2024 Gopal Kumar. This is an open access article distributed under the Creative Commons Attribution License.
