Open AccessStock Market Prediction Using Machine Learning
Author(s) -
Shailendra Nath Gaur,
Rishabh Bhardwaj,
Vinay S. Bansal,
Nidhi Kumari,
Shalley K. Gupta
Publication year - 2019
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit195361
Subject(s) - support vector machine , hyperplane , artificial intelligence , margin (machine learning) , stock market prediction , computer science , machine learning , stock (firearms) , stock market , feature vector , separable space , econometrics , mathematics , engineering , mechanical engineering , paleontology , mathematical analysis , geometry , horse , biology
Stock price prediction is one of the most complex machine learning problems. It depends on a large number of factors which contribute to changes in the supply and demand. In this paper, we propose a stock prediction analysis using machine learning based on support vector machines (SVM), linear regression and reinforcement learning. SVM are favored in applications where text mining is used for market prediction. SVMs can be used for both linearly and non-linearly separable data sets. when the data is linearly separable, SVMs construct a hyperplane on the feature space to distinguish the training tuples in the data such that the margin between the support vectors is maximized. Correlation is used between stock prices of different companies to predict the price of a stock by using technical indicator of highly correlated stocks, not only stock to be predicted.