Open AccessA SURVEY ON STOCK PRICE PREDICTION USING DEEP LEARNING
Author(s) -
L Dushyanth,
Amith Bhat,
D H Harshith,
M S Prajwal,
R Sandeep
Publication year - 2022
Publication title -
international research journal of computer science
Language(s) - English
Resource type - Journals
ISSN - 2393-9842
DOI - 10.26562/irjcs.2022.v0902.002
Subject(s) - stock (firearms) , stock market , stock market prediction , econometrics , stock price , computer science , stock market bubble , restricted stock , economics , financial economics , engineering , series (stratigraphy) , mechanical engineering , paleontology , horse , biology
Stock is a curve with a lot of unknowns. Stock market forecasting is fraught with complications and unpredictability. One of the most challenging and sophisticated methods of doing business is investing in the stock market. Stock forecasting is a difficult and time-consuming activity since the stock market is extremely volatile with stock prices fluctuating due to a variety of variables. Investors nowadays want quick and precise information to make informed decisions, thanks to the rapid growth of technology in stock price prediction. Understanding a company's stock price pattern and estimating its future development and financial growth will be quite advantageous. As the stock is made up of dynamic data, data is the critical source of efficiency. In the current trend of predicting stocks, deep learning is the most popular among the prediction of datasets. To forecast and automate operations, deep learning employs several prediction models and algorithms. The paper briefs about different algorithms and methods used for stock market prediction.