Open AccessA Review of Stock Prediction Using Various Machine Learning Techniques
Author(s) -
Shekhar Paliwal,
Shivang Sharma
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.41572
Subject(s) - computer science , artificial intelligence , machine learning , artificial neural network , stock market prediction , stock market , stock (firearms) , field (mathematics) , deep learning , key (lock) , engineering , mechanical engineering , paleontology , mathematics , computer security , horse , pure mathematics , biology
Abstract: Stock market prediction is a complex task due huge volume of data that keeps on changing, and various uncertainties and interrelated local and global economic factors. The key to successful stock price forecasting is achieving best results with minimum amount of required input data. Thanks to the development of technology, in recent years more and more research in the field of the prediction is being done and it becomes easier for us to make stock price prediction by using various ways that include machine learning, deep learning and many other such technologies. Many exceptional innovations in the field of Machine Learning, have been enforced to develop a short-term prediction model. This paper surveys a number of resources from research papers that have studied on the topic. Keywords: Stock market, machine learning, artificial intelligence, neural networks, technical analysis.