International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
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A Review of Stock Price Prediction Using Machine Learning Techniques Tan Chun Fui1, Tan Lay Hong2 , Ajay Kumar Singh3 1 Senior Lecturer, Faculty of Information Science Technology, Multimedia University-Jalan Ayer Keroh Lama,
Melaka, Malaysia. Senior Lecturer, Universiti Teknikal Malaysia Melaka (UTeM), Fakulti Pengurusan Teknologi Dan Teknousahawanan (FPTT), Centre of Technopreneurship Development (CTeD), 75450 Ayer Keroh, Melaka, Malaysia. 3 Professor, Electronics and Communication Engineering NIIT University, Alwar, Rajasthan India. ---------------------------------------------------------------------***--------------------------------------------------------------------2
Abstract - This paper reviews existing literature on
The motivation behind this study lies in the recognition of the growing importance of precise stock price forecasts and the increasing role of machine learning in this endeavor. With rapid advancements in machine learning technology, there emerges an opportunity to not only understand the theoretical underpinnings of these techniques but also to provide practical guidance for aspiring practitioners. While previous research has contributed valuable insights into machine learning for stock price prediction, there remains a distinct need to distill this knowledge into a cohesive set of guidelines that can serve as a structured entry point for newcomers in the field. Therefore, the main objective of this paper is to provide practical guidelines for beginners entering the field of machine learning for stock price prediction instead of just providing the knowledge of machine learning theories or comparing algorithm advantages and disadvantages.
predicting stock prices using machine learning techniques, emphasizing the growing importance of accurate stock price predictions for making informed financial decisions. The primary goal of this study is to provide practical guidelines for beginners entering the field of machine learning for stock price prediction instead of just providing the knowledge of machine learning or comparing the advantages and disadvantage of the algorithm. The review encompasses various academic journals. For the selection of research papers, the study focuses on publications from 2010 to 2023, a period marked by significant advancements in machine learning. The criteria for choosing these 24 research papers are based on their implementation of different machine learning methods for stock price prediction, along with the presence of results, data processing processes, or algorithms. By examining various machine learning methods employed in stock price prediction and their implementation details, this review aims to distill actionable insights for newcomers. It summarizes key findings and extracts practical guidance, providing novice practitioners with a structured entry point into the world of machine learning for stock price prediction system. Additionally, the paper acknowledges the limitations of current research and suggests potential areas for future exploration, ensuring a comprehensive and informative resource for those venturing into stock price prediction using machine learning.
This research paper aims to bridge the gap between theoretical knowledge and practical implementation in the domain of stock price prediction using machine learning techniques. The primary objective is to conduct an review of 24 research papers published between 2010 and 2023, focusing on their implementation of diverse machine learning methods, data processing techniques, and their demonstration of results, algorithms, or data processing processes. These papers have been meticulously selected to provide a comprehensive understanding of the field.
Key Words: Stock price predictions ·Machine learning techniques · Real-time market data · Financial indicators · Predictive models.
The subsequent sections of this research paper will comprise a thorough exploration of the relevant literature, covering fundamental concepts in stock price markets, the intricacies of machine learning, evaluation metrics, and data processing techniques. Following the literature review, the methodology section will explain the approach taken to conduct this study, including detailed explanations of paper selection criteria and the process for extracting valuable information from each selected research paper.
1.INTRODUCTION The stock market is vital for our economy, influencing how businesses are run and helping people manage their money. It can encourage companies to make better long-term decisions, but also carries significant risky [1]. Market volatility poses challenges for investors and companies, prompting researchers to develop predictive methods to aid wise investment decisions and minimize losses. This is an important and active area of research to minimize the financial risk [1].
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The discussion section will provide the findings from the reviewed research papers, synthesising of their findings, insights, and methodologies. Finally, the conclusion section will summarize of key takeaways, highlight the limitations of existing research, and propose potential avenues for future exploration.
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