Open AccessDeep learning based non-linear regression for Stock Prediction
Author(s) -
Subhash Chand Agrawal
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1116/1/012189
Subject(s) - stock (firearms) , stock exchange , stock market , shareholder , stock market prediction , econometrics , computer science , restricted stock , artificial intelligence , growth stock , stock price , machine learning , economics , finance , engineering , mechanical engineering , paleontology , corporate governance , horse , series (stratigraphy) , biology
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.