Deep learning based non-linear regression for Stock Prediction. Issue 1 (April 2021)

• Record Type: Journal Article
• Authors: Agrawal, Subhash Chand
• Is Part Of: IOP conference series. Volume 1116:Issue 1(2021)
• Publication Date: 2021-04
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1116/1/012189
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: Stock market prediction is an activity to estimate the future value of a stock. The accurate prediction of particular share's future price can lead to significant profit margins for an investor. The efficient market hypothesis states that prices of the stock depend on the available information and price changes, do not consider any hidden information. Therefore, prediction of stock plays a significant role to influence the investor's decisions. It also acts a recommend system for investment related decision in stock market for short term investors and financial suffering system for long term shareholders. In this paper, we propose a stock market prediction system using machine learning algorithms. This paper first explores a few machine learning algorithms for estimating stock value and then proposes a solution that can predict the future stock value with higher accuracy. In this paper, we propose a deep learning based non-linear regression method to predict the stock price. The experiments are performed on two publically available datasets : Tesla Stock Price and New York Stock Exchange which consist of stock data from 2010 to 2020. The analysis of experimentation reveals that the proposed method performed better than existing machine learning based approaches.