Evaluating future stock value asset using machine learning. (2020)

• Record Type: Journal Article
• Authors: Soujanya, R.; Goud, P. Akshith; Bhandwalkar, Abhishek; Kumar, G. Anil
• Is Part Of: Materials today. Volume 33:Part 7(2020)
• Page Start: 4808
• Page End: 4813
• Publication Date: 2020
• Subjects: Stock market -- Stock prediction -- Machine learning -- Linear regression -- Web Scrapping -- Multiple Linear Regression; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2020.08.385
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Stock Trading is one of the intuitive ways through which people make money. It operates just like an auction bridge where purchaser and trader negotiate prices and make trades. Stock trading involves putting our calculative minds and applying for some crunch numbers on the graphs to get the desired profits. But with the advancement of technology stock trading has reached another level. With algorithmic trading forex bots and predictions coming into the picture, people are trying to make much more money than the usual ways which involves going to the market with their gut feeling. This paper will be telling about stock prediction using machine learning as a tool to determine the future value of a stock. There is a lot of quantitative and technical analysis that goes into the stock prediction when done by the stockbrokers which are accurate up to a great extent or sometimes isn't. But there is a better scope to improve our prediction by using certain machine learning models. In this paper, the model will be using the linear regression model to predict stock prices for capitalizations in different markets employing assets with daily and up to date minute frequencies.