Exploring the Future of Stock Market Prediction through Machine Learning: An Extensive Review and Outlook
Sourabh Jain1, Navdeep Kaur Saluja2, Anil Pimplapure3, Rani Sahu4
1Sourabh Jain, Research Scholar, Department of Computer Science and Engineering, Eklavya University, Damoh (M.P), India.
2Dr. Navdeep Kaur Saluja, Professor, Department of Computer Science and Engineering, Eklavya University, Damoh (M.P), India.
3Dr. Anil Pimplapure, Professor, Department of Computer Science and Engineering, Eklvya University, Damoh (M.P), India.
4Dr. Rani Sahu, Associate Professor, Department of Computer Science and Engineering, IES Group of Institutions Bhopal (M.P), India.
Manuscript received on 06 March 2024 | Revised Manuscript received on 05 April 2024 | Manuscript Accepted on 15 April 2024 | Manuscript published on 30 April 2024 | PP: 1-10 | Volume-12 Issue-4, April 2024 | Retrieval Number: 100.1/ijisme.E983713050424 | DOI: 10.35940/ijisme.E9837.12040424
Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A thorough analysis of trends and future directions reveals how machine learning is revolutionizing stock market forecasting. The most recent research on machine learning applications for stock market prediction during the previous 20 years is methodically reviewed in this article. Artificial neural networks, support vector machines, genetic algorithms in conjunction with other methodologies, and hybrid or alternative AI approaches were the categories used to group journal articles. Every category was examined to identify trends, distinct perspectives, constraints, and areas that needed more research. The results provide insightful analysis and suggestions for further study in this developing topic.
Keywords: Stock market forecasting, Machine learning, Artificial neural networks, Support vector machines, Genetic algorithms, Hybrid AI approaches, Systematic literature review, Future research directions.
Scope of the Article: Machine learning